{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Loading and Augmentation Pipeline\n", "\n", "This section of the notebook sets up the data pipeline for our segmentation task. Here's a brief overview:\n", "\n", "1. **Parameters and Paths** \n", " - We define key parameters: image dimensions (`256×256`), batch size (`4`), number of epochs (`500`), and number of folds (`5`) for k-fold cross validation.\n", " - The paths to the images and corresponding masks are specified. We then collect the full file paths for all image and mask files (supporting common image extensions).\n", "\n", "2. **Data Loading** \n", " - The `py_load_image_and_mask` function uses PIL to:\n", " - **Load the image:** Convert to RGB, resize to the target dimensions, and normalize pixel values to [0, 1].\n", " - **Load the mask:** Resize to the target dimensions and convert it into a binary mask (i.e., any non-zero pixel becomes 1).\n", " - This function is wrapped by `load_image_and_mask` using `tf.numpy_function` so it can be integrated into a TensorFlow `tf.data.Dataset` pipeline.\n", "\n", "3. **Data Augmentation** \n", " - The `augment` function applies random transformations to the image/mask pair:\n", " - Random horizontal and vertical flips.\n", " - Random rotation by multiples of 90°.\n", " - Random brightness and contrast adjustments (applied only to the image).\n", " - This augmentation increases data variability, which is crucial for improving model generalization, especially with limited data.\n", "\n", "4. **Dataset Creation** \n", " - The `create_dataset` function takes lists of image and mask file paths and builds a `tf.data.Dataset`:\n", " - It maps the loading function to each pair.\n", " - If augmentation is enabled (`augment_flag=True`), it applies the augmentation function.\n", " - The dataset is batched and prefetched to optimize training performance.\n", "\n", "This pipeline ensures that the images and masks are consistently preprocessed and augmented before being fed into the model for training or evaluation.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import os\n", "import cv2\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "import tensorflow.keras.backend as K\n", "from tensorflow.keras import layers, Model, callbacks\n", "from sklearn.model_selection import train_test_split\n", "from PIL import Image\n", "\n", "# -------------------------\n", "# 1. Parameters and Paths\n", "# -------------------------\n", "IMG_HEIGHT = 256\n", "IMG_WIDTH = 256\n", "BATCH_SIZE = 4\n", "EPOCHS = 500\n", "\n", "images_dir = r\"C:\\Users\\Niepraschk\\Desktop\\Fuer Souman\\ground_truth\\images\"\n", "masks_dir = r\"C:\\Users\\Niepraschk\\Desktop\\Fuer Souman\\ground_truth\\masks\"\n", "\n", "# -------------------------\n", "# 2. Compute Instance Boundary\n", "# -------------------------\n", "def compute_instance_boundaries(label_mask, dilation_ratio=0.01):\n", " boundary_mask = np.zeros_like(label_mask, dtype=np.uint8)\n", " kernel_size = int(round(np.sqrt(label_mask.shape[0] * label_mask.shape[1]) * dilation_ratio))\n", " kernel_size += 1 if kernel_size % 2 == 0 else 0\n", " kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))\n", "\n", " unique_labels = np.unique(label_mask)\n", " for label in unique_labels:\n", " if label == 0:\n", " continue\n", " obj_mask = (label_mask == label).astype(np.uint8)\n", " dilated = cv2.dilate(obj_mask, kernel, iterations=1)\n", " eroded = cv2.erode(obj_mask, kernel, iterations=1)\n", " boundary = dilated - eroded\n", " boundary_mask = np.bitwise_or(boundary_mask, boundary)\n", "\n", " return (boundary_mask > 0).astype(np.float32)\n", "\n", "# -------------------------\n", "# 3. Losses and Metrics\n", "# -------------------------\n", "def dice_loss(y_true, y_pred, smooth=1e-6):\n", " y_true_f = K.flatten(y_true)\n", " y_pred_f = K.flatten(y_pred)\n", " intersection = K.sum(y_true_f * y_pred_f)\n", " return 1 - (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n", "\n", "def bce_dice_loss(y_true, y_pred):\n", " return tf.keras.losses.binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)\n", "\n", "def iou_coef(y_true, y_pred, smooth=1e-6):\n", " y_true_f = K.flatten(y_true)\n", " y_pred_f = K.flatten(y_pred)\n", " intersection = K.sum(y_true_f * y_pred_f)\n", " union = K.sum(y_true_f) + K.sum(y_pred_f) - intersection\n", " return (intersection + smooth) / (union + smooth)\n", "\n", "# -------------------------\n", "# 4. Data Loading\n", "# -------------------------\n", "def py_load_image_and_masks(image_path, mask_path):\n", " image_path = image_path.decode(\"utf-8\")\n", " mask_path = mask_path.decode(\"utf-8\")\n", "\n", " image = Image.open(image_path).convert(\"RGB\")\n", " image = image.resize((IMG_WIDTH, IMG_HEIGHT))\n", " image_array = np.array(image, dtype=np.float32) / 255.0\n", "\n", " mask = Image.open(mask_path)\n", " mask = mask.resize((IMG_WIDTH, IMG_HEIGHT), resample=Image.NEAREST)\n", " label_mask = np.array(mask, dtype=np.int32)\n", "\n", " mask_bin = (label_mask > 0).astype(np.float32)\n", " mask_bin = np.expand_dims(mask_bin, axis=-1)\n", "\n", " boundary = compute_instance_boundaries(label_mask)\n", " boundary = np.expand_dims(boundary, axis=-1)\n", "\n", " return image_array, mask_bin, boundary.astype(np.float32)\n", "\n", "def load_image_and_masks(image_path, mask_path):\n", " image, mask, boundary = tf.numpy_function(\n", " py_load_image_and_masks, [image_path, mask_path], [tf.float32, tf.float32, tf.float32]\n", " )\n", " image.set_shape([IMG_HEIGHT, IMG_WIDTH, 3])\n", " mask.set_shape([IMG_HEIGHT, IMG_WIDTH, 1])\n", " boundary.set_shape([IMG_HEIGHT, IMG_WIDTH, 1])\n", " return image, (mask, boundary)\n", "\n", "@tf.function\n", "def augment(image, masks):\n", " mask, boundary = masks\n", "\n", " # --- 1) existing rotate + photometric jitter ---\n", " k = tf.random.uniform([], 0, 4, tf.int32)\n", " image = tf.image.rot90(image, k)\n", " mask = tf.image.rot90(mask, k)\n", " boundary= tf.image.rot90(boundary,k)\n", " image = tf.image.random_brightness(image, max_delta=0.1)\n", " image = tf.image.random_contrast(image, lower=0.9, upper=1.1)\n", "\n", " # --- 2) new random zoom-in 50% of the time ---\n", " if tf.random.uniform([]) > 0.5:\n", " # pick a random zoom factor between 0.5× and 1.0×\n", " scale = tf.random.uniform([], 0.5, 1.0)\n", " new_h = tf.cast(scale * IMG_HEIGHT, tf.int32)\n", " new_w = tf.cast(scale * IMG_WIDTH, tf.int32)\n", " # stack image+mask+boundary so we crop all identically\n", " stacked = tf.concat([image, mask, boundary], axis=-1) # shape [H,W,5]\n", " cropped = tf.image.random_crop(stacked, size=[new_h, new_w, 5])\n", " # resize each back to full size\n", " image = tf.image.resize(cropped[..., :3], [IMG_HEIGHT, IMG_WIDTH])\n", " mask = tf.image.resize(cropped[..., 3:4], [IMG_HEIGHT, IMG_WIDTH],\n", " method='nearest')\n", " boundary = tf.image.resize(cropped[..., 4:5], [IMG_HEIGHT, IMG_WIDTH],\n", " method='nearest')\n", "\n", " return image, (mask, boundary)\n", "\n", "def create_dataset(img_list, mask_list, batch_size, augment_flag=False):\n", " ds = tf.data.Dataset.from_tensor_slices((img_list, mask_list))\n", " ds = ds.map(load_image_and_masks, num_parallel_calls=tf.data.AUTOTUNE)\n", " if augment_flag:\n", " ds = ds.map(augment, num_parallel_calls=tf.data.AUTOTUNE)\n", " ds = ds.batch(batch_size).prefetch(tf.data.AUTOTUNE)\n", " return ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Architecture, Loss Function, and K-Fold Cross Validation\n", "\n", "**Model Architecture: Improved U-Net with Dropout and Regularization**\n", "\n", "- **U-Net Design:** \n", " The network follows a U-Net structure with an encoder (down-sampling) and a decoder (up-sampling) path. \n", "- **Regularization:** \n", " Each convolutional layer uses L2 regularization to help reduce overfitting. \n", "- **Dropout Layers:** \n", " Dropout is applied after pooling and within decoder blocks to further combat overfitting, especially important with a small dataset.\n", "\n", "**Loss Function and IoU Metric**\n", "\n", "- **Combined Loss (`bce_dice_loss`):** \n", " The loss is a sum of binary crossentropy and Dice loss. Binary crossentropy measures pixel-wise classification error, while Dice loss focuses on the overlap between the predicted and true masks.\n", "- **IoU Metric:** \n", " The Intersection over Union (IoU) metric is defined to quantify segmentation performance by comparing the area of overlap with the area of union between the predicted and true masks.\n", "\n", "**K-Fold Cross Validation**\n", "\n", "- **Purpose:** \n", " With a limited dataset, K-fold cross validation maximizes the use of available data and provides a robust estimate of model performance.\n", "- **Implementation:** \n", " The data is split into 5 folds. In each iteration:\n", " - One fold is used for validation, and the remaining folds for training.\n", " - The model is trained with the improved U-Net architecture and evaluated on the validation fold.\n", " - A `ModelCheckpoint` callback saves the best model (lowest validation loss) for that fold.\n", "- **Outcome:** \n", " After training on all folds, IoU scores are aggregated (average and standard deviation) to assess overall model performance.\n", "\n", "This integrated pipeline—from an improved model architecture to robust evaluation using K-fold cross validation—ensures that we get reliable performance metrics and make the most of our limited data.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "📊 Total images: 1148\n", "📊 Total masks: 1148\n", "📂 Training: 918\n", "📂 Validation: 230\n", "Epoch 1/500\n", "230/230 [==============================] - ETA: 0s - loss: 1.0337 - mask_output_loss: 0.6264 - boundary_output_loss: 0.8146 - mask_output_iou_coef: 0.4743 - boundary_output_iou_coef: 0.2555\n", "Epoch 1: val_loss improved from inf to 0.73615, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 492s 2s/step - loss: 1.0337 - mask_output_loss: 0.6264 - boundary_output_loss: 0.8146 - mask_output_iou_coef: 0.4743 - boundary_output_iou_coef: 0.2555 - val_loss: 0.7362 - val_mask_output_loss: 0.4351 - val_boundary_output_loss: 0.6021 - val_mask_output_iou_coef: 0.6053 - val_boundary_output_iou_coef: 0.4138\n", "Epoch 2/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.7258 - mask_output_loss: 0.4364 - boundary_output_loss: 0.5788 - mask_output_iou_coef: 0.6031 - boundary_output_iou_coef: 0.4114\n", "Epoch 2: val_loss improved from 0.73615 to 0.64465, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 492s 2s/step - loss: 0.7258 - mask_output_loss: 0.4364 - boundary_output_loss: 0.5788 - mask_output_iou_coef: 0.6031 - boundary_output_iou_coef: 0.4114 - val_loss: 0.6447 - val_mask_output_loss: 0.3768 - val_boundary_output_loss: 0.5358 - val_mask_output_iou_coef: 0.6460 - val_boundary_output_iou_coef: 0.4751\n", "Epoch 3/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.6118 - mask_output_loss: 0.3614 - boundary_output_loss: 0.5007 - mask_output_iou_coef: 0.6577 - boundary_output_iou_coef: 0.4721\n", "Epoch 3: val_loss improved from 0.64465 to 0.55937, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 513s 2s/step - loss: 0.6118 - mask_output_loss: 0.3614 - boundary_output_loss: 0.5007 - mask_output_iou_coef: 0.6577 - boundary_output_iou_coef: 0.4721 - val_loss: 0.5594 - val_mask_output_loss: 0.3219 - val_boundary_output_loss: 0.4749 - val_mask_output_iou_coef: 0.6906 - val_boundary_output_iou_coef: 0.5144\n", "Epoch 4/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.5899 - mask_output_loss: 0.3521 - boundary_output_loss: 0.4755 - mask_output_iou_coef: 0.6696 - boundary_output_iou_coef: 0.4949\n", "Epoch 4: val_loss improved from 0.55937 to 0.54991, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 529s 2s/step - loss: 0.5899 - mask_output_loss: 0.3521 - boundary_output_loss: 0.4755 - mask_output_iou_coef: 0.6696 - boundary_output_iou_coef: 0.4949 - val_loss: 0.5499 - val_mask_output_loss: 0.3241 - val_boundary_output_loss: 0.4517 - val_mask_output_iou_coef: 0.6880 - val_boundary_output_iou_coef: 0.5320\n", "Epoch 5/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.5682 - mask_output_loss: 0.3380 - boundary_output_loss: 0.4604 - mask_output_iou_coef: 0.6793 - boundary_output_iou_coef: 0.5074\n", "Epoch 5: val_loss improved from 0.54991 to 0.52468, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 537s 2s/step - loss: 0.5682 - mask_output_loss: 0.3380 - boundary_output_loss: 0.4604 - mask_output_iou_coef: 0.6793 - boundary_output_iou_coef: 0.5074 - val_loss: 0.5247 - val_mask_output_loss: 0.3118 - val_boundary_output_loss: 0.4258 - val_mask_output_iou_coef: 0.7229 - val_boundary_output_iou_coef: 0.5611\n", "Epoch 6/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.5476 - mask_output_loss: 0.3258 - boundary_output_loss: 0.4436 - mask_output_iou_coef: 0.6900 - boundary_output_iou_coef: 0.5209\n", "Epoch 6: val_loss improved from 0.52468 to 0.50750, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 522s 2s/step - loss: 0.5476 - mask_output_loss: 0.3258 - boundary_output_loss: 0.4436 - mask_output_iou_coef: 0.6900 - boundary_output_iou_coef: 0.5209 - val_loss: 0.5075 - val_mask_output_loss: 0.2917 - val_boundary_output_loss: 0.4316 - val_mask_output_iou_coef: 0.7209 - val_boundary_output_iou_coef: 0.5566\n", "Epoch 7/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.5380 - mask_output_loss: 0.3195 - boundary_output_loss: 0.4369 - mask_output_iou_coef: 0.6938 - boundary_output_iou_coef: 0.5264\n", "Epoch 7: val_loss improved from 0.50750 to 0.48952, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 530s 2s/step - loss: 0.5380 - mask_output_loss: 0.3195 - boundary_output_loss: 0.4369 - mask_output_iou_coef: 0.6938 - boundary_output_iou_coef: 0.5264 - val_loss: 0.4895 - val_mask_output_loss: 0.2853 - val_boundary_output_loss: 0.4085 - val_mask_output_iou_coef: 0.7333 - val_boundary_output_iou_coef: 0.5771\n", "Epoch 8/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.5227 - mask_output_loss: 0.3093 - boundary_output_loss: 0.4269 - mask_output_iou_coef: 0.7027 - boundary_output_iou_coef: 0.5338\n", "Epoch 8: val_loss did not improve from 0.48952\n", "230/230 [==============================] - 538s 2s/step - loss: 0.5227 - mask_output_loss: 0.3093 - boundary_output_loss: 0.4269 - mask_output_iou_coef: 0.7027 - boundary_output_iou_coef: 0.5338 - val_loss: 0.4919 - val_mask_output_loss: 0.2834 - val_boundary_output_loss: 0.4169 - val_mask_output_iou_coef: 0.7298 - val_boundary_output_iou_coef: 0.5714\n", "Epoch 9/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4949 - mask_output_loss: 0.2901 - boundary_output_loss: 0.4095 - mask_output_iou_coef: 0.7142 - boundary_output_iou_coef: 0.5490\n", "Epoch 9: val_loss improved from 0.48952 to 0.45675, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 536s 2s/step - loss: 0.4949 - mask_output_loss: 0.2901 - boundary_output_loss: 0.4095 - mask_output_iou_coef: 0.7142 - boundary_output_iou_coef: 0.5490 - val_loss: 0.4568 - val_mask_output_loss: 0.2658 - val_boundary_output_loss: 0.3820 - val_mask_output_iou_coef: 0.7323 - val_boundary_output_iou_coef: 0.5797\n", "Epoch 10/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4880 - mask_output_loss: 0.2874 - boundary_output_loss: 0.4012 - mask_output_iou_coef: 0.7171 - boundary_output_iou_coef: 0.5549\n", "Epoch 10: val_loss improved from 0.45675 to 0.43694, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 524s 2s/step - loss: 0.4880 - mask_output_loss: 0.2874 - boundary_output_loss: 0.4012 - mask_output_iou_coef: 0.7171 - boundary_output_iou_coef: 0.5549 - val_loss: 0.4369 - val_mask_output_loss: 0.2511 - val_boundary_output_loss: 0.3718 - val_mask_output_iou_coef: 0.7413 - val_boundary_output_iou_coef: 0.5935\n", "Epoch 11/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4711 - mask_output_loss: 0.2745 - boundary_output_loss: 0.3932 - mask_output_iou_coef: 0.7262 - boundary_output_iou_coef: 0.5622\n", "Epoch 11: val_loss improved from 0.43694 to 0.40768, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 507s 2s/step - loss: 0.4711 - mask_output_loss: 0.2745 - boundary_output_loss: 0.3932 - mask_output_iou_coef: 0.7262 - boundary_output_iou_coef: 0.5622 - val_loss: 0.4077 - val_mask_output_loss: 0.2370 - val_boundary_output_loss: 0.3413 - val_mask_output_iou_coef: 0.7684 - val_boundary_output_iou_coef: 0.6192\n", "Epoch 12/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4798 - mask_output_loss: 0.2840 - boundary_output_loss: 0.3916 - mask_output_iou_coef: 0.7234 - boundary_output_iou_coef: 0.5633\n", "Epoch 12: val_loss did not improve from 0.40768\n", "230/230 [==============================] - 510s 2s/step - loss: 0.4798 - mask_output_loss: 0.2840 - boundary_output_loss: 0.3916 - mask_output_iou_coef: 0.7234 - boundary_output_iou_coef: 0.5633 - val_loss: 0.4155 - val_mask_output_loss: 0.2410 - val_boundary_output_loss: 0.3490 - val_mask_output_iou_coef: 0.7613 - val_boundary_output_iou_coef: 0.6161\n", "Epoch 13/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4545 - mask_output_loss: 0.2657 - boundary_output_loss: 0.3777 - mask_output_iou_coef: 0.7351 - boundary_output_iou_coef: 0.5756\n", "Epoch 13: val_loss improved from 0.40768 to 0.38320, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 526s 2s/step - loss: 0.4545 - mask_output_loss: 0.2657 - boundary_output_loss: 0.3777 - mask_output_iou_coef: 0.7351 - boundary_output_iou_coef: 0.5756 - val_loss: 0.3832 - val_mask_output_loss: 0.2164 - val_boundary_output_loss: 0.3336 - val_mask_output_iou_coef: 0.7741 - val_boundary_output_iou_coef: 0.6282\n", "Epoch 14/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4436 - mask_output_loss: 0.2592 - boundary_output_loss: 0.3687 - mask_output_iou_coef: 0.7405 - boundary_output_iou_coef: 0.5837\n", "Epoch 14: val_loss improved from 0.38320 to 0.36883, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 523s 2s/step - loss: 0.4436 - mask_output_loss: 0.2592 - boundary_output_loss: 0.3687 - mask_output_iou_coef: 0.7405 - boundary_output_iou_coef: 0.5837 - val_loss: 0.3688 - val_mask_output_loss: 0.2113 - val_boundary_output_loss: 0.3150 - val_mask_output_iou_coef: 0.7801 - val_boundary_output_iou_coef: 0.6402\n", "Epoch 15/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4570 - mask_output_loss: 0.2683 - boundary_output_loss: 0.3773 - mask_output_iou_coef: 0.7351 - boundary_output_iou_coef: 0.5780\n", "Epoch 15: val_loss improved from 0.36883 to 0.36222, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 526s 2s/step - loss: 0.4570 - mask_output_loss: 0.2683 - boundary_output_loss: 0.3773 - mask_output_iou_coef: 0.7351 - boundary_output_iou_coef: 0.5780 - val_loss: 0.3622 - val_mask_output_loss: 0.2095 - val_boundary_output_loss: 0.3055 - val_mask_output_iou_coef: 0.7853 - val_boundary_output_iou_coef: 0.6531\n", "Epoch 16/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4308 - mask_output_loss: 0.2515 - boundary_output_loss: 0.3587 - mask_output_iou_coef: 0.7466 - boundary_output_iou_coef: 0.5929\n", "Epoch 16: val_loss improved from 0.36222 to 0.35320, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 554s 2s/step - loss: 0.4308 - mask_output_loss: 0.2515 - boundary_output_loss: 0.3587 - mask_output_iou_coef: 0.7466 - boundary_output_iou_coef: 0.5929 - val_loss: 0.3532 - val_mask_output_loss: 0.2019 - val_boundary_output_loss: 0.3027 - val_mask_output_iou_coef: 0.7941 - val_boundary_output_iou_coef: 0.6557\n", "Epoch 17/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4332 - mask_output_loss: 0.2567 - boundary_output_loss: 0.3529 - mask_output_iou_coef: 0.7421 - boundary_output_iou_coef: 0.5973\n", "Epoch 17: val_loss improved from 0.35320 to 0.34913, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 525s 2s/step - loss: 0.4332 - mask_output_loss: 0.2567 - boundary_output_loss: 0.3529 - mask_output_iou_coef: 0.7421 - boundary_output_iou_coef: 0.5973 - val_loss: 0.3491 - val_mask_output_loss: 0.2005 - val_boundary_output_loss: 0.2972 - val_mask_output_iou_coef: 0.7956 - val_boundary_output_iou_coef: 0.6620\n", "Epoch 18/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4297 - mask_output_loss: 0.2543 - boundary_output_loss: 0.3508 - mask_output_iou_coef: 0.7461 - boundary_output_iou_coef: 0.5995\n", "Epoch 18: val_loss improved from 0.34913 to 0.34690, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 511s 2s/step - loss: 0.4297 - mask_output_loss: 0.2543 - boundary_output_loss: 0.3508 - mask_output_iou_coef: 0.7461 - boundary_output_iou_coef: 0.5995 - val_loss: 0.3469 - val_mask_output_loss: 0.2021 - val_boundary_output_loss: 0.2896 - val_mask_output_iou_coef: 0.7954 - val_boundary_output_iou_coef: 0.6686\n", "Epoch 19/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4270 - mask_output_loss: 0.2512 - boundary_output_loss: 0.3516 - mask_output_iou_coef: 0.7497 - boundary_output_iou_coef: 0.5997\n", "Epoch 19: val_loss improved from 0.34690 to 0.32684, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 516s 2s/step - loss: 0.4270 - mask_output_loss: 0.2512 - boundary_output_loss: 0.3516 - mask_output_iou_coef: 0.7497 - boundary_output_iou_coef: 0.5997 - val_loss: 0.3268 - val_mask_output_loss: 0.1882 - val_boundary_output_loss: 0.2772 - val_mask_output_iou_coef: 0.8077 - val_boundary_output_iou_coef: 0.6856\n", "Epoch 20/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4363 - mask_output_loss: 0.2585 - boundary_output_loss: 0.3557 - mask_output_iou_coef: 0.7455 - boundary_output_iou_coef: 0.5974\n", "Epoch 20: val_loss did not improve from 0.32684\n", "230/230 [==============================] - 513s 2s/step - loss: 0.4363 - mask_output_loss: 0.2585 - boundary_output_loss: 0.3557 - mask_output_iou_coef: 0.7455 - boundary_output_iou_coef: 0.5974 - val_loss: 0.3342 - val_mask_output_loss: 0.1956 - val_boundary_output_loss: 0.2771 - val_mask_output_iou_coef: 0.7948 - val_boundary_output_iou_coef: 0.6774\n", "Epoch 21/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4203 - mask_output_loss: 0.2484 - boundary_output_loss: 0.3439 - mask_output_iou_coef: 0.7516 - boundary_output_iou_coef: 0.6063\n", "Epoch 21: val_loss did not improve from 0.32684\n", "230/230 [==============================] - 519s 2s/step - loss: 0.4203 - mask_output_loss: 0.2484 - boundary_output_loss: 0.3439 - mask_output_iou_coef: 0.7516 - boundary_output_iou_coef: 0.6063 - val_loss: 0.3348 - val_mask_output_loss: 0.1976 - val_boundary_output_loss: 0.2743 - val_mask_output_iou_coef: 0.7994 - val_boundary_output_iou_coef: 0.6864\n", "Epoch 22/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4207 - mask_output_loss: 0.2497 - boundary_output_loss: 0.3420 - mask_output_iou_coef: 0.7506 - boundary_output_iou_coef: 0.6084\n", "Epoch 22: val_loss improved from 0.32684 to 0.30948, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 522s 2s/step - loss: 0.4207 - mask_output_loss: 0.2497 - boundary_output_loss: 0.3420 - mask_output_iou_coef: 0.7506 - boundary_output_iou_coef: 0.6084 - val_loss: 0.3095 - val_mask_output_loss: 0.1780 - val_boundary_output_loss: 0.2630 - val_mask_output_iou_coef: 0.8125 - val_boundary_output_iou_coef: 0.6917\n", "Epoch 23/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4214 - mask_output_loss: 0.2511 - boundary_output_loss: 0.3406 - mask_output_iou_coef: 0.7518 - boundary_output_iou_coef: 0.6098\n", "Epoch 23: val_loss improved from 0.30948 to 0.30922, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 522s 2s/step - loss: 0.4214 - mask_output_loss: 0.2511 - boundary_output_loss: 0.3406 - mask_output_iou_coef: 0.7518 - boundary_output_iou_coef: 0.6098 - val_loss: 0.3092 - val_mask_output_loss: 0.1776 - val_boundary_output_loss: 0.2632 - val_mask_output_iou_coef: 0.8112 - val_boundary_output_iou_coef: 0.6949\n", "Epoch 24/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4012 - mask_output_loss: 0.2333 - boundary_output_loss: 0.3357 - mask_output_iou_coef: 0.7622 - boundary_output_iou_coef: 0.6149\n", "Epoch 24: val_loss improved from 0.30922 to 0.30072, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 517s 2s/step - loss: 0.4012 - mask_output_loss: 0.2333 - boundary_output_loss: 0.3357 - mask_output_iou_coef: 0.7622 - boundary_output_iou_coef: 0.6149 - val_loss: 0.3007 - val_mask_output_loss: 0.1720 - val_boundary_output_loss: 0.2574 - val_mask_output_iou_coef: 0.8192 - val_boundary_output_iou_coef: 0.6975\n", "Epoch 25/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4051 - mask_output_loss: 0.2392 - boundary_output_loss: 0.3316 - mask_output_iou_coef: 0.7589 - boundary_output_iou_coef: 0.6184\n", "Epoch 25: val_loss did not improve from 0.30072\n", "230/230 [==============================] - 517s 2s/step - loss: 0.4051 - mask_output_loss: 0.2392 - boundary_output_loss: 0.3316 - mask_output_iou_coef: 0.7589 - boundary_output_iou_coef: 0.6184 - val_loss: 0.3128 - val_mask_output_loss: 0.1822 - val_boundary_output_loss: 0.2612 - val_mask_output_iou_coef: 0.8134 - val_boundary_output_iou_coef: 0.6999\n", "Epoch 26/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4050 - mask_output_loss: 0.2360 - boundary_output_loss: 0.3380 - mask_output_iou_coef: 0.7626 - boundary_output_iou_coef: 0.6137\n", "Epoch 26: val_loss did not improve from 0.30072\n", "230/230 [==============================] - 515s 2s/step - loss: 0.4050 - mask_output_loss: 0.2360 - boundary_output_loss: 0.3380 - mask_output_iou_coef: 0.7626 - boundary_output_iou_coef: 0.6137 - val_loss: 0.3139 - val_mask_output_loss: 0.1796 - val_boundary_output_loss: 0.2686 - val_mask_output_iou_coef: 0.8156 - val_boundary_output_iou_coef: 0.6935\n", "Epoch 27/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4052 - mask_output_loss: 0.2349 - boundary_output_loss: 0.3407 - mask_output_iou_coef: 0.7625 - boundary_output_iou_coef: 0.6111\n", "Epoch 27: val_loss did not improve from 0.30072\n", "230/230 [==============================] - 508s 2s/step - loss: 0.4052 - mask_output_loss: 0.2349 - boundary_output_loss: 0.3407 - mask_output_iou_coef: 0.7625 - boundary_output_iou_coef: 0.6111 - val_loss: 0.3124 - val_mask_output_loss: 0.1821 - val_boundary_output_loss: 0.2606 - val_mask_output_iou_coef: 0.8147 - val_boundary_output_iou_coef: 0.6991\n", "Epoch 28/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3982 - mask_output_loss: 0.2319 - boundary_output_loss: 0.3327 - mask_output_iou_coef: 0.7654 - boundary_output_iou_coef: 0.6178\n", "Epoch 28: val_loss did not improve from 0.30072\n", "230/230 [==============================] - 513s 2s/step - loss: 0.3982 - mask_output_loss: 0.2319 - boundary_output_loss: 0.3327 - mask_output_iou_coef: 0.7654 - boundary_output_iou_coef: 0.6178 - val_loss: 0.3011 - val_mask_output_loss: 0.1744 - val_boundary_output_loss: 0.2535 - val_mask_output_iou_coef: 0.8179 - val_boundary_output_iou_coef: 0.7039\n", "Epoch 29/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3927 - mask_output_loss: 0.2317 - boundary_output_loss: 0.3221 - mask_output_iou_coef: 0.7661 - boundary_output_iou_coef: 0.6279\n", "Epoch 29: val_loss improved from 0.30072 to 0.29729, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 510s 2s/step - loss: 0.3927 - mask_output_loss: 0.2317 - boundary_output_loss: 0.3221 - mask_output_iou_coef: 0.7661 - boundary_output_iou_coef: 0.6279 - val_loss: 0.2973 - val_mask_output_loss: 0.1733 - val_boundary_output_loss: 0.2479 - val_mask_output_iou_coef: 0.8215 - val_boundary_output_iou_coef: 0.7131\n", "Epoch 30/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3840 - mask_output_loss: 0.2235 - boundary_output_loss: 0.3211 - mask_output_iou_coef: 0.7732 - boundary_output_iou_coef: 0.6282\n", "Epoch 30: val_loss improved from 0.29729 to 0.28960, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 517s 2s/step - loss: 0.3840 - mask_output_loss: 0.2235 - boundary_output_loss: 0.3211 - mask_output_iou_coef: 0.7732 - boundary_output_iou_coef: 0.6282 - val_loss: 0.2896 - val_mask_output_loss: 0.1670 - val_boundary_output_loss: 0.2452 - val_mask_output_iou_coef: 0.8274 - val_boundary_output_iou_coef: 0.7156\n", "Epoch 31/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.4014 - mask_output_loss: 0.2372 - boundary_output_loss: 0.3285 - mask_output_iou_coef: 0.7650 - boundary_output_iou_coef: 0.6222\n", "Epoch 31: val_loss did not improve from 0.28960\n", "230/230 [==============================] - 514s 2s/step - loss: 0.4014 - mask_output_loss: 0.2372 - boundary_output_loss: 0.3285 - mask_output_iou_coef: 0.7650 - boundary_output_iou_coef: 0.6222 - val_loss: 0.2942 - val_mask_output_loss: 0.1707 - val_boundary_output_loss: 0.2470 - val_mask_output_iou_coef: 0.8240 - val_boundary_output_iou_coef: 0.7164\n", "Epoch 32/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3870 - mask_output_loss: 0.2267 - boundary_output_loss: 0.3206 - mask_output_iou_coef: 0.7715 - boundary_output_iou_coef: 0.6291\n", "Epoch 32: val_loss improved from 0.28960 to 0.28244, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 517s 2s/step - loss: 0.3870 - mask_output_loss: 0.2267 - boundary_output_loss: 0.3206 - mask_output_iou_coef: 0.7715 - boundary_output_iou_coef: 0.6291 - val_loss: 0.2824 - val_mask_output_loss: 0.1593 - val_boundary_output_loss: 0.2462 - val_mask_output_iou_coef: 0.8351 - val_boundary_output_iou_coef: 0.7134\n", "Epoch 33/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3833 - mask_output_loss: 0.2240 - boundary_output_loss: 0.3186 - mask_output_iou_coef: 0.7729 - boundary_output_iou_coef: 0.6307\n", "Epoch 33: val_loss did not improve from 0.28244\n", "230/230 [==============================] - 517s 2s/step - loss: 0.3833 - mask_output_loss: 0.2240 - boundary_output_loss: 0.3186 - mask_output_iou_coef: 0.7729 - boundary_output_iou_coef: 0.6307 - val_loss: 0.2862 - val_mask_output_loss: 0.1628 - val_boundary_output_loss: 0.2469 - val_mask_output_iou_coef: 0.8316 - val_boundary_output_iou_coef: 0.7153\n", "Epoch 34/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3727 - mask_output_loss: 0.2167 - boundary_output_loss: 0.3120 - mask_output_iou_coef: 0.7778 - boundary_output_iou_coef: 0.6369\n", "Epoch 34: val_loss did not improve from 0.28244\n", "230/230 [==============================] - 511s 2s/step - loss: 0.3727 - mask_output_loss: 0.2167 - boundary_output_loss: 0.3120 - mask_output_iou_coef: 0.7778 - boundary_output_iou_coef: 0.6369 - val_loss: 0.2833 - val_mask_output_loss: 0.1629 - val_boundary_output_loss: 0.2407 - val_mask_output_iou_coef: 0.8304 - val_boundary_output_iou_coef: 0.7209\n", "Epoch 35/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3787 - mask_output_loss: 0.2223 - boundary_output_loss: 0.3130 - mask_output_iou_coef: 0.7763 - boundary_output_iou_coef: 0.6373\n", "Epoch 35: val_loss improved from 0.28244 to 0.27628, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 510s 2s/step - loss: 0.3787 - mask_output_loss: 0.2223 - boundary_output_loss: 0.3130 - mask_output_iou_coef: 0.7763 - boundary_output_iou_coef: 0.6373 - val_loss: 0.2763 - val_mask_output_loss: 0.1582 - val_boundary_output_loss: 0.2362 - val_mask_output_iou_coef: 0.8376 - val_boundary_output_iou_coef: 0.7249\n", "Epoch 36/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3801 - mask_output_loss: 0.2226 - boundary_output_loss: 0.3150 - mask_output_iou_coef: 0.7750 - boundary_output_iou_coef: 0.6346\n", "Epoch 36: val_loss did not improve from 0.27628\n", "230/230 [==============================] - 512s 2s/step - loss: 0.3801 - mask_output_loss: 0.2226 - boundary_output_loss: 0.3150 - mask_output_iou_coef: 0.7750 - boundary_output_iou_coef: 0.6346 - val_loss: 0.2789 - val_mask_output_loss: 0.1591 - val_boundary_output_loss: 0.2397 - val_mask_output_iou_coef: 0.8363 - val_boundary_output_iou_coef: 0.7197\n", "Epoch 37/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3841 - mask_output_loss: 0.2276 - boundary_output_loss: 0.3131 - mask_output_iou_coef: 0.7704 - boundary_output_iou_coef: 0.6355\n", "Epoch 37: val_loss improved from 0.27628 to 0.26947, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 517s 2s/step - loss: 0.3841 - mask_output_loss: 0.2276 - boundary_output_loss: 0.3131 - mask_output_iou_coef: 0.7704 - boundary_output_iou_coef: 0.6355 - val_loss: 0.2695 - val_mask_output_loss: 0.1528 - val_boundary_output_loss: 0.2334 - val_mask_output_iou_coef: 0.8420 - val_boundary_output_iou_coef: 0.7262\n", "Epoch 38/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3675 - mask_output_loss: 0.2143 - boundary_output_loss: 0.3064 - mask_output_iou_coef: 0.7808 - boundary_output_iou_coef: 0.6428\n", "Epoch 38: val_loss did not improve from 0.26947\n", "230/230 [==============================] - 518s 2s/step - loss: 0.3675 - mask_output_loss: 0.2143 - boundary_output_loss: 0.3064 - mask_output_iou_coef: 0.7808 - boundary_output_iou_coef: 0.6428 - val_loss: 0.2698 - val_mask_output_loss: 0.1547 - val_boundary_output_loss: 0.2302 - val_mask_output_iou_coef: 0.8415 - val_boundary_output_iou_coef: 0.7290\n", "Epoch 39/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3745 - mask_output_loss: 0.2191 - boundary_output_loss: 0.3108 - mask_output_iou_coef: 0.7796 - boundary_output_iou_coef: 0.6401\n", "Epoch 39: val_loss improved from 0.26947 to 0.26919, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 513s 2s/step - loss: 0.3745 - mask_output_loss: 0.2191 - boundary_output_loss: 0.3108 - mask_output_iou_coef: 0.7796 - boundary_output_iou_coef: 0.6401 - val_loss: 0.2692 - val_mask_output_loss: 0.1540 - val_boundary_output_loss: 0.2305 - val_mask_output_iou_coef: 0.8343 - val_boundary_output_iou_coef: 0.7286\n", "Epoch 40/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3702 - mask_output_loss: 0.2175 - boundary_output_loss: 0.3055 - mask_output_iou_coef: 0.7791 - boundary_output_iou_coef: 0.6433\n", "Epoch 40: val_loss did not improve from 0.26919\n", "230/230 [==============================] - 524s 2s/step - loss: 0.3702 - mask_output_loss: 0.2175 - boundary_output_loss: 0.3055 - mask_output_iou_coef: 0.7791 - boundary_output_iou_coef: 0.6433 - val_loss: 0.2697 - val_mask_output_loss: 0.1534 - val_boundary_output_loss: 0.2325 - val_mask_output_iou_coef: 0.8347 - val_boundary_output_iou_coef: 0.7229\n", "Epoch 41/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3749 - mask_output_loss: 0.2216 - boundary_output_loss: 0.3067 - mask_output_iou_coef: 0.7772 - boundary_output_iou_coef: 0.6433\n", "Epoch 41: val_loss improved from 0.26919 to 0.26707, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 519s 2s/step - loss: 0.3749 - mask_output_loss: 0.2216 - boundary_output_loss: 0.3067 - mask_output_iou_coef: 0.7772 - boundary_output_iou_coef: 0.6433 - val_loss: 0.2671 - val_mask_output_loss: 0.1525 - val_boundary_output_loss: 0.2291 - val_mask_output_iou_coef: 0.8433 - val_boundary_output_iou_coef: 0.7342\n", "Epoch 42/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3679 - mask_output_loss: 0.2168 - boundary_output_loss: 0.3022 - mask_output_iou_coef: 0.7794 - boundary_output_iou_coef: 0.6473\n", "Epoch 42: val_loss improved from 0.26707 to 0.26511, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 514s 2s/step - loss: 0.3679 - mask_output_loss: 0.2168 - boundary_output_loss: 0.3022 - mask_output_iou_coef: 0.7794 - boundary_output_iou_coef: 0.6473 - val_loss: 0.2651 - val_mask_output_loss: 0.1502 - val_boundary_output_loss: 0.2299 - val_mask_output_iou_coef: 0.8373 - val_boundary_output_iou_coef: 0.7271\n", "Epoch 43/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3612 - mask_output_loss: 0.2096 - boundary_output_loss: 0.3033 - mask_output_iou_coef: 0.7858 - boundary_output_iou_coef: 0.6463\n", "Epoch 43: val_loss improved from 0.26511 to 0.25789, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 516s 2s/step - loss: 0.3612 - mask_output_loss: 0.2096 - boundary_output_loss: 0.3033 - mask_output_iou_coef: 0.7858 - boundary_output_iou_coef: 0.6463 - val_loss: 0.2579 - val_mask_output_loss: 0.1454 - val_boundary_output_loss: 0.2250 - val_mask_output_iou_coef: 0.8463 - val_boundary_output_iou_coef: 0.7337\n", "Epoch 44/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3646 - mask_output_loss: 0.2141 - boundary_output_loss: 0.3009 - mask_output_iou_coef: 0.7822 - boundary_output_iou_coef: 0.6486\n", "Epoch 44: val_loss improved from 0.25789 to 0.25426, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 507s 2s/step - loss: 0.3646 - mask_output_loss: 0.2141 - boundary_output_loss: 0.3009 - mask_output_iou_coef: 0.7822 - boundary_output_iou_coef: 0.6486 - val_loss: 0.2543 - val_mask_output_loss: 0.1447 - val_boundary_output_loss: 0.2191 - val_mask_output_iou_coef: 0.8500 - val_boundary_output_iou_coef: 0.7422\n", "Epoch 45/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3626 - mask_output_loss: 0.2137 - boundary_output_loss: 0.2978 - mask_output_iou_coef: 0.7840 - boundary_output_iou_coef: 0.6510\n", "Epoch 45: val_loss did not improve from 0.25426\n", "230/230 [==============================] - 507s 2s/step - loss: 0.3626 - mask_output_loss: 0.2137 - boundary_output_loss: 0.2978 - mask_output_iou_coef: 0.7840 - boundary_output_iou_coef: 0.6510 - val_loss: 0.2559 - val_mask_output_loss: 0.1458 - val_boundary_output_loss: 0.2201 - val_mask_output_iou_coef: 0.8448 - val_boundary_output_iou_coef: 0.7433\n", "Epoch 46/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3631 - mask_output_loss: 0.2123 - boundary_output_loss: 0.3016 - mask_output_iou_coef: 0.7832 - boundary_output_iou_coef: 0.6480\n", "Epoch 46: val_loss improved from 0.25426 to 0.25381, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 518s 2s/step - loss: 0.3631 - mask_output_loss: 0.2123 - boundary_output_loss: 0.3016 - mask_output_iou_coef: 0.7832 - boundary_output_iou_coef: 0.6480 - val_loss: 0.2538 - val_mask_output_loss: 0.1437 - val_boundary_output_loss: 0.2202 - val_mask_output_iou_coef: 0.8488 - val_boundary_output_iou_coef: 0.7416\n", "Epoch 47/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3587 - mask_output_loss: 0.2105 - boundary_output_loss: 0.2965 - mask_output_iou_coef: 0.7854 - boundary_output_iou_coef: 0.6529\n", "Epoch 47: val_loss improved from 0.25381 to 0.25078, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 517s 2s/step - loss: 0.3587 - mask_output_loss: 0.2105 - boundary_output_loss: 0.2965 - mask_output_iou_coef: 0.7854 - boundary_output_iou_coef: 0.6529 - val_loss: 0.2508 - val_mask_output_loss: 0.1426 - val_boundary_output_loss: 0.2164 - val_mask_output_iou_coef: 0.8530 - val_boundary_output_iou_coef: 0.7431\n", "Epoch 48/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3666 - mask_output_loss: 0.2161 - boundary_output_loss: 0.3011 - mask_output_iou_coef: 0.7818 - boundary_output_iou_coef: 0.6480\n", "Epoch 48: val_loss did not improve from 0.25078\n", "230/230 [==============================] - 519s 2s/step - loss: 0.3666 - mask_output_loss: 0.2161 - boundary_output_loss: 0.3011 - mask_output_iou_coef: 0.7818 - boundary_output_iou_coef: 0.6480 - val_loss: 0.2807 - val_mask_output_loss: 0.1603 - val_boundary_output_loss: 0.2409 - val_mask_output_iou_coef: 0.8260 - val_boundary_output_iou_coef: 0.7177\n", "Epoch 49/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3861 - mask_output_loss: 0.2249 - boundary_output_loss: 0.3224 - mask_output_iou_coef: 0.7731 - boundary_output_iou_coef: 0.6312\n", "Epoch 49: val_loss did not improve from 0.25078\n", "230/230 [==============================] - 529s 2s/step - loss: 0.3861 - mask_output_loss: 0.2249 - boundary_output_loss: 0.3224 - mask_output_iou_coef: 0.7731 - boundary_output_iou_coef: 0.6312 - val_loss: 0.2581 - val_mask_output_loss: 0.1460 - val_boundary_output_loss: 0.2242 - val_mask_output_iou_coef: 0.8470 - val_boundary_output_iou_coef: 0.7344\n", "Epoch 50/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3651 - mask_output_loss: 0.2128 - boundary_output_loss: 0.3045 - mask_output_iou_coef: 0.7846 - boundary_output_iou_coef: 0.6449\n", "Epoch 50: val_loss did not improve from 0.25078\n", "230/230 [==============================] - 525s 2s/step - loss: 0.3651 - mask_output_loss: 0.2128 - boundary_output_loss: 0.3045 - mask_output_iou_coef: 0.7846 - boundary_output_iou_coef: 0.6449 - val_loss: 0.2545 - val_mask_output_loss: 0.1439 - val_boundary_output_loss: 0.2211 - val_mask_output_iou_coef: 0.8495 - val_boundary_output_iou_coef: 0.7404\n", "Epoch 51/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3644 - mask_output_loss: 0.2161 - boundary_output_loss: 0.2966 - mask_output_iou_coef: 0.7851 - boundary_output_iou_coef: 0.6546\n", "Epoch 51: val_loss did not improve from 0.25078\n", "230/230 [==============================] - 526s 2s/step - loss: 0.3644 - mask_output_loss: 0.2161 - boundary_output_loss: 0.2966 - mask_output_iou_coef: 0.7851 - boundary_output_iou_coef: 0.6546 - val_loss: 0.2531 - val_mask_output_loss: 0.1436 - val_boundary_output_loss: 0.2191 - val_mask_output_iou_coef: 0.8525 - val_boundary_output_iou_coef: 0.7471\n", "Epoch 52/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3540 - mask_output_loss: 0.2051 - boundary_output_loss: 0.2977 - mask_output_iou_coef: 0.7896 - boundary_output_iou_coef: 0.6521\n", "Epoch 52: val_loss improved from 0.25078 to 0.24978, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 522s 2s/step - loss: 0.3540 - mask_output_loss: 0.2051 - boundary_output_loss: 0.2977 - mask_output_iou_coef: 0.7896 - boundary_output_iou_coef: 0.6521 - val_loss: 0.2498 - val_mask_output_loss: 0.1422 - val_boundary_output_loss: 0.2151 - val_mask_output_iou_coef: 0.8520 - val_boundary_output_iou_coef: 0.7471\n", "Epoch 53/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3570 - mask_output_loss: 0.2077 - boundary_output_loss: 0.2987 - mask_output_iou_coef: 0.7875 - boundary_output_iou_coef: 0.6508\n", "Epoch 53: val_loss improved from 0.24978 to 0.24849, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 520s 2s/step - loss: 0.3570 - mask_output_loss: 0.2077 - boundary_output_loss: 0.2987 - mask_output_iou_coef: 0.7875 - boundary_output_iou_coef: 0.6508 - val_loss: 0.2485 - val_mask_output_loss: 0.1414 - val_boundary_output_loss: 0.2143 - val_mask_output_iou_coef: 0.8506 - val_boundary_output_iou_coef: 0.7461\n", "Epoch 54/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3569 - mask_output_loss: 0.2083 - boundary_output_loss: 0.2971 - mask_output_iou_coef: 0.7890 - boundary_output_iou_coef: 0.6524\n", "Epoch 54: val_loss did not improve from 0.24849\n", "230/230 [==============================] - 521s 2s/step - loss: 0.3569 - mask_output_loss: 0.2083 - boundary_output_loss: 0.2971 - mask_output_iou_coef: 0.7890 - boundary_output_iou_coef: 0.6524 - val_loss: 0.2573 - val_mask_output_loss: 0.1474 - val_boundary_output_loss: 0.2198 - val_mask_output_iou_coef: 0.8432 - val_boundary_output_iou_coef: 0.7402\n", "Epoch 55/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3564 - mask_output_loss: 0.2080 - boundary_output_loss: 0.2967 - mask_output_iou_coef: 0.7880 - boundary_output_iou_coef: 0.6532\n", "Epoch 55: val_loss improved from 0.24849 to 0.24260, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 521s 2s/step - loss: 0.3564 - mask_output_loss: 0.2080 - boundary_output_loss: 0.2967 - mask_output_iou_coef: 0.7880 - boundary_output_iou_coef: 0.6532 - val_loss: 0.2426 - val_mask_output_loss: 0.1367 - val_boundary_output_loss: 0.2118 - val_mask_output_iou_coef: 0.8526 - val_boundary_output_iou_coef: 0.7495\n", "Epoch 56/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3524 - mask_output_loss: 0.2058 - boundary_output_loss: 0.2931 - mask_output_iou_coef: 0.7908 - boundary_output_iou_coef: 0.6561\n", "Epoch 56: val_loss did not improve from 0.24260\n", "230/230 [==============================] - 517s 2s/step - loss: 0.3524 - mask_output_loss: 0.2058 - boundary_output_loss: 0.2931 - mask_output_iou_coef: 0.7908 - boundary_output_iou_coef: 0.6561 - val_loss: 0.2445 - val_mask_output_loss: 0.1377 - val_boundary_output_loss: 0.2136 - val_mask_output_iou_coef: 0.8548 - val_boundary_output_iou_coef: 0.7467\n", "Epoch 57/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3469 - mask_output_loss: 0.2014 - boundary_output_loss: 0.2908 - mask_output_iou_coef: 0.7939 - boundary_output_iou_coef: 0.6585\n", "Epoch 57: val_loss did not improve from 0.24260\n", "230/230 [==============================] - 515s 2s/step - loss: 0.3469 - mask_output_loss: 0.2014 - boundary_output_loss: 0.2908 - mask_output_iou_coef: 0.7939 - boundary_output_iou_coef: 0.6585 - val_loss: 0.2446 - val_mask_output_loss: 0.1390 - val_boundary_output_loss: 0.2111 - val_mask_output_iou_coef: 0.8529 - val_boundary_output_iou_coef: 0.7487\n", "Epoch 58/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3508 - mask_output_loss: 0.2066 - boundary_output_loss: 0.2884 - mask_output_iou_coef: 0.7883 - boundary_output_iou_coef: 0.6608\n", "Epoch 58: val_loss did not improve from 0.24260\n", "230/230 [==============================] - 517s 2s/step - loss: 0.3508 - mask_output_loss: 0.2066 - boundary_output_loss: 0.2884 - mask_output_iou_coef: 0.7883 - boundary_output_iou_coef: 0.6608 - val_loss: 0.2448 - val_mask_output_loss: 0.1377 - val_boundary_output_loss: 0.2142 - val_mask_output_iou_coef: 0.8534 - val_boundary_output_iou_coef: 0.7456\n", "Epoch 59/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3508 - mask_output_loss: 0.2053 - boundary_output_loss: 0.2909 - mask_output_iou_coef: 0.7917 - boundary_output_iou_coef: 0.6581\n", "Epoch 59: val_loss improved from 0.24260 to 0.23816, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 519s 2s/step - loss: 0.3508 - mask_output_loss: 0.2053 - boundary_output_loss: 0.2909 - mask_output_iou_coef: 0.7917 - boundary_output_iou_coef: 0.6581 - val_loss: 0.2382 - val_mask_output_loss: 0.1333 - val_boundary_output_loss: 0.2098 - val_mask_output_iou_coef: 0.8587 - val_boundary_output_iou_coef: 0.7535\n", "Epoch 60/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3407 - mask_output_loss: 0.1993 - boundary_output_loss: 0.2828 - mask_output_iou_coef: 0.7949 - boundary_output_iou_coef: 0.6657\n", "Epoch 60: val_loss improved from 0.23816 to 0.23690, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 520s 2s/step - loss: 0.3407 - mask_output_loss: 0.1993 - boundary_output_loss: 0.2828 - mask_output_iou_coef: 0.7949 - boundary_output_iou_coef: 0.6657 - val_loss: 0.2369 - val_mask_output_loss: 0.1337 - val_boundary_output_loss: 0.2065 - val_mask_output_iou_coef: 0.8612 - val_boundary_output_iou_coef: 0.7555\n", "Epoch 61/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3442 - mask_output_loss: 0.1989 - boundary_output_loss: 0.2905 - mask_output_iou_coef: 0.7966 - boundary_output_iou_coef: 0.6588\n", "Epoch 61: val_loss did not improve from 0.23690\n", "230/230 [==============================] - 516s 2s/step - loss: 0.3442 - mask_output_loss: 0.1989 - boundary_output_loss: 0.2905 - mask_output_iou_coef: 0.7966 - boundary_output_iou_coef: 0.6588 - val_loss: 0.2388 - val_mask_output_loss: 0.1359 - val_boundary_output_loss: 0.2058 - val_mask_output_iou_coef: 0.8560 - val_boundary_output_iou_coef: 0.7531\n", "Epoch 62/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3405 - mask_output_loss: 0.1979 - boundary_output_loss: 0.2853 - mask_output_iou_coef: 0.7963 - boundary_output_iou_coef: 0.6639\n", "Epoch 62: val_loss did not improve from 0.23690\n", "230/230 [==============================] - 513s 2s/step - loss: 0.3405 - mask_output_loss: 0.1979 - boundary_output_loss: 0.2853 - mask_output_iou_coef: 0.7963 - boundary_output_iou_coef: 0.6639 - val_loss: 0.2407 - val_mask_output_loss: 0.1354 - val_boundary_output_loss: 0.2105 - val_mask_output_iou_coef: 0.8610 - val_boundary_output_iou_coef: 0.7535\n", "Epoch 63/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3459 - mask_output_loss: 0.1999 - boundary_output_loss: 0.2920 - mask_output_iou_coef: 0.7958 - boundary_output_iou_coef: 0.6583\n", "Epoch 63: val_loss did not improve from 0.23690\n", "230/230 [==============================] - 510s 2s/step - loss: 0.3459 - mask_output_loss: 0.1999 - boundary_output_loss: 0.2920 - mask_output_iou_coef: 0.7958 - boundary_output_iou_coef: 0.6583 - val_loss: 0.2408 - val_mask_output_loss: 0.1360 - val_boundary_output_loss: 0.2097 - val_mask_output_iou_coef: 0.8549 - val_boundary_output_iou_coef: 0.7510\n", "Epoch 64/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3359 - mask_output_loss: 0.1942 - boundary_output_loss: 0.2835 - mask_output_iou_coef: 0.7985 - boundary_output_iou_coef: 0.6657\n", "Epoch 64: val_loss did not improve from 0.23690\n", "230/230 [==============================] - 495s 2s/step - loss: 0.3359 - mask_output_loss: 0.1942 - boundary_output_loss: 0.2835 - mask_output_iou_coef: 0.7985 - boundary_output_iou_coef: 0.6657 - val_loss: 0.2428 - val_mask_output_loss: 0.1359 - val_boundary_output_loss: 0.2138 - val_mask_output_iou_coef: 0.8586 - val_boundary_output_iou_coef: 0.7512\n", "Epoch 65/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3289 - mask_output_loss: 0.1890 - boundary_output_loss: 0.2798 - mask_output_iou_coef: 0.8043 - boundary_output_iou_coef: 0.6688\n", "Epoch 65: val_loss improved from 0.23690 to 0.23620, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 499s 2s/step - loss: 0.3289 - mask_output_loss: 0.1890 - boundary_output_loss: 0.2798 - mask_output_iou_coef: 0.8043 - boundary_output_iou_coef: 0.6688 - val_loss: 0.2362 - val_mask_output_loss: 0.1324 - val_boundary_output_loss: 0.2077 - val_mask_output_iou_coef: 0.8562 - val_boundary_output_iou_coef: 0.7534\n", "Epoch 66/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3379 - mask_output_loss: 0.1952 - boundary_output_loss: 0.2853 - mask_output_iou_coef: 0.7984 - boundary_output_iou_coef: 0.6639\n", "Epoch 66: val_loss did not improve from 0.23620\n", "230/230 [==============================] - 524s 2s/step - loss: 0.3379 - mask_output_loss: 0.1952 - boundary_output_loss: 0.2853 - mask_output_iou_coef: 0.7984 - boundary_output_iou_coef: 0.6639 - val_loss: 0.2430 - val_mask_output_loss: 0.1379 - val_boundary_output_loss: 0.2103 - val_mask_output_iou_coef: 0.8542 - val_boundary_output_iou_coef: 0.7513\n", "Epoch 67/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3466 - mask_output_loss: 0.2049 - boundary_output_loss: 0.2834 - mask_output_iou_coef: 0.7928 - boundary_output_iou_coef: 0.6657\n", "Epoch 67: val_loss improved from 0.23620 to 0.23247, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 526s 2s/step - loss: 0.3466 - mask_output_loss: 0.2049 - boundary_output_loss: 0.2834 - mask_output_iou_coef: 0.7928 - boundary_output_iou_coef: 0.6657 - val_loss: 0.2325 - val_mask_output_loss: 0.1307 - val_boundary_output_loss: 0.2036 - val_mask_output_iou_coef: 0.8633 - val_boundary_output_iou_coef: 0.7593\n", "Epoch 68/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3455 - mask_output_loss: 0.2027 - boundary_output_loss: 0.2857 - mask_output_iou_coef: 0.7923 - boundary_output_iou_coef: 0.6625\n", "Epoch 68: val_loss improved from 0.23247 to 0.23173, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 519s 2s/step - loss: 0.3455 - mask_output_loss: 0.2027 - boundary_output_loss: 0.2857 - mask_output_iou_coef: 0.7923 - boundary_output_iou_coef: 0.6625 - val_loss: 0.2317 - val_mask_output_loss: 0.1294 - val_boundary_output_loss: 0.2047 - val_mask_output_iou_coef: 0.8616 - val_boundary_output_iou_coef: 0.7549\n", "Epoch 69/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3699 - mask_output_loss: 0.2178 - boundary_output_loss: 0.3042 - mask_output_iou_coef: 0.7830 - boundary_output_iou_coef: 0.6472\n", "Epoch 69: val_loss did not improve from 0.23173\n", "230/230 [==============================] - 553s 2s/step - loss: 0.3699 - mask_output_loss: 0.2178 - boundary_output_loss: 0.3042 - mask_output_iou_coef: 0.7830 - boundary_output_iou_coef: 0.6472 - val_loss: 0.2453 - val_mask_output_loss: 0.1371 - val_boundary_output_loss: 0.2164 - val_mask_output_iou_coef: 0.8557 - val_boundary_output_iou_coef: 0.7463\n", "Epoch 70/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3397 - mask_output_loss: 0.1973 - boundary_output_loss: 0.2849 - mask_output_iou_coef: 0.7980 - boundary_output_iou_coef: 0.6648\n", "Epoch 70: val_loss improved from 0.23173 to 0.23068, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 520s 2s/step - loss: 0.3397 - mask_output_loss: 0.1973 - boundary_output_loss: 0.2849 - mask_output_iou_coef: 0.7980 - boundary_output_iou_coef: 0.6648 - val_loss: 0.2307 - val_mask_output_loss: 0.1296 - val_boundary_output_loss: 0.2022 - val_mask_output_iou_coef: 0.8623 - val_boundary_output_iou_coef: 0.7571\n", "Epoch 71/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3330 - mask_output_loss: 0.1935 - boundary_output_loss: 0.2790 - mask_output_iou_coef: 0.8016 - boundary_output_iou_coef: 0.6700\n", "Epoch 71: val_loss did not improve from 0.23068\n", "230/230 [==============================] - 521s 2s/step - loss: 0.3330 - mask_output_loss: 0.1935 - boundary_output_loss: 0.2790 - mask_output_iou_coef: 0.8016 - boundary_output_iou_coef: 0.6700 - val_loss: 0.2353 - val_mask_output_loss: 0.1316 - val_boundary_output_loss: 0.2072 - val_mask_output_iou_coef: 0.8639 - val_boundary_output_iou_coef: 0.7562\n", "Epoch 72/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3395 - mask_output_loss: 0.1979 - boundary_output_loss: 0.2832 - mask_output_iou_coef: 0.7981 - boundary_output_iou_coef: 0.6667\n", "Epoch 72: val_loss did not improve from 0.23068\n", "230/230 [==============================] - 523s 2s/step - loss: 0.3395 - mask_output_loss: 0.1979 - boundary_output_loss: 0.2832 - mask_output_iou_coef: 0.7981 - boundary_output_iou_coef: 0.6667 - val_loss: 0.2338 - val_mask_output_loss: 0.1317 - val_boundary_output_loss: 0.2042 - val_mask_output_iou_coef: 0.8624 - val_boundary_output_iou_coef: 0.7555\n", "Epoch 73/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3192 - mask_output_loss: 0.1830 - boundary_output_loss: 0.2725 - mask_output_iou_coef: 0.8100 - boundary_output_iou_coef: 0.6769\n", "Epoch 73: val_loss did not improve from 0.23068\n", "230/230 [==============================] - 518s 2s/step - loss: 0.3192 - mask_output_loss: 0.1830 - boundary_output_loss: 0.2725 - mask_output_iou_coef: 0.8100 - boundary_output_iou_coef: 0.6769 - val_loss: 0.2347 - val_mask_output_loss: 0.1316 - val_boundary_output_loss: 0.2061 - val_mask_output_iou_coef: 0.8627 - val_boundary_output_iou_coef: 0.7566\n", "Epoch 74/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3366 - mask_output_loss: 0.1974 - boundary_output_loss: 0.2784 - mask_output_iou_coef: 0.7998 - boundary_output_iou_coef: 0.6711\n", "Epoch 74: val_loss improved from 0.23068 to 0.22804, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 528s 2s/step - loss: 0.3366 - mask_output_loss: 0.1974 - boundary_output_loss: 0.2784 - mask_output_iou_coef: 0.7998 - boundary_output_iou_coef: 0.6711 - val_loss: 0.2280 - val_mask_output_loss: 0.1275 - val_boundary_output_loss: 0.2010 - val_mask_output_iou_coef: 0.8668 - val_boundary_output_iou_coef: 0.7606\n", "Epoch 75/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3255 - mask_output_loss: 0.1878 - boundary_output_loss: 0.2754 - mask_output_iou_coef: 0.8062 - boundary_output_iou_coef: 0.6737\n", "Epoch 75: val_loss did not improve from 0.22804\n", "230/230 [==============================] - 526s 2s/step - loss: 0.3255 - mask_output_loss: 0.1878 - boundary_output_loss: 0.2754 - mask_output_iou_coef: 0.8062 - boundary_output_iou_coef: 0.6737 - val_loss: 0.2325 - val_mask_output_loss: 0.1313 - val_boundary_output_loss: 0.2024 - val_mask_output_iou_coef: 0.8570 - val_boundary_output_iou_coef: 0.7549\n", "Epoch 76/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3258 - mask_output_loss: 0.1889 - boundary_output_loss: 0.2737 - mask_output_iou_coef: 0.8053 - boundary_output_iou_coef: 0.6760\n", "Epoch 76: val_loss did not improve from 0.22804\n", "230/230 [==============================] - 525s 2s/step - loss: 0.3258 - mask_output_loss: 0.1889 - boundary_output_loss: 0.2737 - mask_output_iou_coef: 0.8053 - boundary_output_iou_coef: 0.6760 - val_loss: 0.2335 - val_mask_output_loss: 0.1311 - val_boundary_output_loss: 0.2047 - val_mask_output_iou_coef: 0.8635 - val_boundary_output_iou_coef: 0.7566\n", "Epoch 77/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3362 - mask_output_loss: 0.1956 - boundary_output_loss: 0.2813 - mask_output_iou_coef: 0.7995 - boundary_output_iou_coef: 0.6683\n", "Epoch 77: val_loss improved from 0.22804 to 0.22639, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 523s 2s/step - loss: 0.3362 - mask_output_loss: 0.1956 - boundary_output_loss: 0.2813 - mask_output_iou_coef: 0.7995 - boundary_output_iou_coef: 0.6683 - val_loss: 0.2264 - val_mask_output_loss: 0.1279 - val_boundary_output_loss: 0.1970 - val_mask_output_iou_coef: 0.8675 - val_boundary_output_iou_coef: 0.7654\n", "Epoch 78/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3308 - mask_output_loss: 0.1925 - boundary_output_loss: 0.2765 - mask_output_iou_coef: 0.8019 - boundary_output_iou_coef: 0.6724\n", "Epoch 78: val_loss did not improve from 0.22639\n", "230/230 [==============================] - 529s 2s/step - loss: 0.3308 - mask_output_loss: 0.1925 - boundary_output_loss: 0.2765 - mask_output_iou_coef: 0.8019 - boundary_output_iou_coef: 0.6724 - val_loss: 0.2323 - val_mask_output_loss: 0.1310 - val_boundary_output_loss: 0.2025 - val_mask_output_iou_coef: 0.8595 - val_boundary_output_iou_coef: 0.7599\n", "Epoch 79/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3327 - mask_output_loss: 0.1943 - boundary_output_loss: 0.2767 - mask_output_iou_coef: 0.8017 - boundary_output_iou_coef: 0.6722\n", "Epoch 79: val_loss did not improve from 0.22639\n", "230/230 [==============================] - 524s 2s/step - loss: 0.3327 - mask_output_loss: 0.1943 - boundary_output_loss: 0.2767 - mask_output_iou_coef: 0.8017 - boundary_output_iou_coef: 0.6722 - val_loss: 0.2360 - val_mask_output_loss: 0.1322 - val_boundary_output_loss: 0.2075 - val_mask_output_iou_coef: 0.8622 - val_boundary_output_iou_coef: 0.7565\n", "Epoch 80/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3344 - mask_output_loss: 0.1957 - boundary_output_loss: 0.2774 - mask_output_iou_coef: 0.8002 - boundary_output_iou_coef: 0.6717\n", "Epoch 80: val_loss did not improve from 0.22639\n", "230/230 [==============================] - 521s 2s/step - loss: 0.3344 - mask_output_loss: 0.1957 - boundary_output_loss: 0.2774 - mask_output_iou_coef: 0.8002 - boundary_output_iou_coef: 0.6717 - val_loss: 0.2352 - val_mask_output_loss: 0.1321 - val_boundary_output_loss: 0.2063 - val_mask_output_iou_coef: 0.8563 - val_boundary_output_iou_coef: 0.7584\n", "Epoch 81/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3448 - mask_output_loss: 0.2010 - boundary_output_loss: 0.2875 - mask_output_iou_coef: 0.7959 - boundary_output_iou_coef: 0.6635\n", "Epoch 81: val_loss did not improve from 0.22639\n", "230/230 [==============================] - 527s 2s/step - loss: 0.3448 - mask_output_loss: 0.2010 - boundary_output_loss: 0.2875 - mask_output_iou_coef: 0.7959 - boundary_output_iou_coef: 0.6635 - val_loss: 0.2541 - val_mask_output_loss: 0.1432 - val_boundary_output_loss: 0.2219 - val_mask_output_iou_coef: 0.8521 - val_boundary_output_iou_coef: 0.7459\n", "Epoch 82/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3392 - mask_output_loss: 0.1957 - boundary_output_loss: 0.2870 - mask_output_iou_coef: 0.8000 - boundary_output_iou_coef: 0.6635\n", "Epoch 82: val_loss did not improve from 0.22639\n", "230/230 [==============================] - 525s 2s/step - loss: 0.3392 - mask_output_loss: 0.1957 - boundary_output_loss: 0.2870 - mask_output_iou_coef: 0.8000 - boundary_output_iou_coef: 0.6635 - val_loss: 0.2299 - val_mask_output_loss: 0.1288 - val_boundary_output_loss: 0.2021 - val_mask_output_iou_coef: 0.8669 - val_boundary_output_iou_coef: 0.7608\n", "Epoch 83/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3332 - mask_output_loss: 0.1942 - boundary_output_loss: 0.2780 - mask_output_iou_coef: 0.8029 - boundary_output_iou_coef: 0.6723\n", "Epoch 83: val_loss improved from 0.22639 to 0.22343, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 524s 2s/step - loss: 0.3332 - mask_output_loss: 0.1942 - boundary_output_loss: 0.2780 - mask_output_iou_coef: 0.8029 - boundary_output_iou_coef: 0.6723 - val_loss: 0.2234 - val_mask_output_loss: 0.1238 - val_boundary_output_loss: 0.1992 - val_mask_output_iou_coef: 0.8666 - val_boundary_output_iou_coef: 0.7610\n", "Epoch 84/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3417 - mask_output_loss: 0.1998 - boundary_output_loss: 0.2838 - mask_output_iou_coef: 0.7964 - boundary_output_iou_coef: 0.6659\n", "Epoch 84: val_loss did not improve from 0.22343\n", "230/230 [==============================] - 529s 2s/step - loss: 0.3417 - mask_output_loss: 0.1998 - boundary_output_loss: 0.2838 - mask_output_iou_coef: 0.7964 - boundary_output_iou_coef: 0.6659 - val_loss: 0.2362 - val_mask_output_loss: 0.1324 - val_boundary_output_loss: 0.2076 - val_mask_output_iou_coef: 0.8621 - val_boundary_output_iou_coef: 0.7550\n", "Epoch 85/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3170 - mask_output_loss: 0.1814 - boundary_output_loss: 0.2710 - mask_output_iou_coef: 0.8125 - boundary_output_iou_coef: 0.6789\n", "Epoch 85: val_loss did not improve from 0.22343\n", "230/230 [==============================] - 518s 2s/step - loss: 0.3170 - mask_output_loss: 0.1814 - boundary_output_loss: 0.2710 - mask_output_iou_coef: 0.8125 - boundary_output_iou_coef: 0.6789 - val_loss: 0.2388 - val_mask_output_loss: 0.1349 - val_boundary_output_loss: 0.2078 - val_mask_output_iou_coef: 0.8635 - val_boundary_output_iou_coef: 0.7577\n", "Epoch 86/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3325 - mask_output_loss: 0.1959 - boundary_output_loss: 0.2732 - mask_output_iou_coef: 0.8015 - boundary_output_iou_coef: 0.6760\n", "Epoch 86: val_loss did not improve from 0.22343\n", "230/230 [==============================] - 551s 2s/step - loss: 0.3325 - mask_output_loss: 0.1959 - boundary_output_loss: 0.2732 - mask_output_iou_coef: 0.8015 - boundary_output_iou_coef: 0.6760 - val_loss: 0.2281 - val_mask_output_loss: 0.1287 - val_boundary_output_loss: 0.1988 - val_mask_output_iou_coef: 0.8664 - val_boundary_output_iou_coef: 0.7644\n", "Epoch 87/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3315 - mask_output_loss: 0.1932 - boundary_output_loss: 0.2767 - mask_output_iou_coef: 0.8019 - boundary_output_iou_coef: 0.6730\n", "Epoch 87: val_loss did not improve from 0.22343\n", "230/230 [==============================] - 543s 2s/step - loss: 0.3315 - mask_output_loss: 0.1932 - boundary_output_loss: 0.2767 - mask_output_iou_coef: 0.8019 - boundary_output_iou_coef: 0.6730 - val_loss: 0.2245 - val_mask_output_loss: 0.1251 - val_boundary_output_loss: 0.1987 - val_mask_output_iou_coef: 0.8672 - val_boundary_output_iou_coef: 0.7615\n", "Epoch 88/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3166 - mask_output_loss: 0.1829 - boundary_output_loss: 0.2674 - mask_output_iou_coef: 0.8106 - boundary_output_iou_coef: 0.6813\n", "Epoch 88: val_loss improved from 0.22343 to 0.21731, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 532s 2s/step - loss: 0.3166 - mask_output_loss: 0.1829 - boundary_output_loss: 0.2674 - mask_output_iou_coef: 0.8106 - boundary_output_iou_coef: 0.6813 - val_loss: 0.2173 - val_mask_output_loss: 0.1210 - val_boundary_output_loss: 0.1926 - val_mask_output_iou_coef: 0.8730 - val_boundary_output_iou_coef: 0.7699\n", "Epoch 89/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3335 - mask_output_loss: 0.1966 - boundary_output_loss: 0.2738 - mask_output_iou_coef: 0.8017 - boundary_output_iou_coef: 0.6752\n", "Epoch 89: val_loss did not improve from 0.21731\n", "230/230 [==============================] - 530s 2s/step - loss: 0.3335 - mask_output_loss: 0.1966 - boundary_output_loss: 0.2738 - mask_output_iou_coef: 0.8017 - boundary_output_iou_coef: 0.6752 - val_loss: 0.2238 - val_mask_output_loss: 0.1268 - val_boundary_output_loss: 0.1941 - val_mask_output_iou_coef: 0.8657 - val_boundary_output_iou_coef: 0.7667\n", "Epoch 90/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3197 - mask_output_loss: 0.1844 - boundary_output_loss: 0.2705 - mask_output_iou_coef: 0.8099 - boundary_output_iou_coef: 0.6787\n", "Epoch 90: val_loss did not improve from 0.21731\n", "230/230 [==============================] - 535s 2s/step - loss: 0.3197 - mask_output_loss: 0.1844 - boundary_output_loss: 0.2705 - mask_output_iou_coef: 0.8099 - boundary_output_iou_coef: 0.6787 - val_loss: 0.2279 - val_mask_output_loss: 0.1285 - val_boundary_output_loss: 0.1990 - val_mask_output_iou_coef: 0.8696 - val_boundary_output_iou_coef: 0.7633\n", "Epoch 91/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3272 - mask_output_loss: 0.1903 - boundary_output_loss: 0.2738 - mask_output_iou_coef: 0.8057 - boundary_output_iou_coef: 0.6766\n", "Epoch 91: val_loss did not improve from 0.21731\n", "230/230 [==============================] - 534s 2s/step - loss: 0.3272 - mask_output_loss: 0.1903 - boundary_output_loss: 0.2738 - mask_output_iou_coef: 0.8057 - boundary_output_iou_coef: 0.6766 - val_loss: 0.2203 - val_mask_output_loss: 0.1230 - val_boundary_output_loss: 0.1945 - val_mask_output_iou_coef: 0.8709 - val_boundary_output_iou_coef: 0.7662\n", "Epoch 92/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3220 - mask_output_loss: 0.1851 - boundary_output_loss: 0.2737 - mask_output_iou_coef: 0.8090 - boundary_output_iou_coef: 0.6762\n", "Epoch 92: val_loss did not improve from 0.21731\n", "230/230 [==============================] - 527s 2s/step - loss: 0.3220 - mask_output_loss: 0.1851 - boundary_output_loss: 0.2737 - mask_output_iou_coef: 0.8090 - boundary_output_iou_coef: 0.6762 - val_loss: 0.2246 - val_mask_output_loss: 0.1265 - val_boundary_output_loss: 0.1962 - val_mask_output_iou_coef: 0.8672 - val_boundary_output_iou_coef: 0.7639\n", "Epoch 93/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3270 - mask_output_loss: 0.1909 - boundary_output_loss: 0.2722 - mask_output_iou_coef: 0.8032 - boundary_output_iou_coef: 0.6769\n", "Epoch 93: val_loss did not improve from 0.21731\n", "230/230 [==============================] - 523s 2s/step - loss: 0.3270 - mask_output_loss: 0.1909 - boundary_output_loss: 0.2722 - mask_output_iou_coef: 0.8032 - boundary_output_iou_coef: 0.6769 - val_loss: 0.2235 - val_mask_output_loss: 0.1245 - val_boundary_output_loss: 0.1980 - val_mask_output_iou_coef: 0.8659 - val_boundary_output_iou_coef: 0.7611\n", "Epoch 94/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3360 - mask_output_loss: 0.1955 - boundary_output_loss: 0.2812 - mask_output_iou_coef: 0.7991 - boundary_output_iou_coef: 0.6684\n", "Epoch 94: val_loss did not improve from 0.21731\n", "230/230 [==============================] - 532s 2s/step - loss: 0.3360 - mask_output_loss: 0.1955 - boundary_output_loss: 0.2812 - mask_output_iou_coef: 0.7991 - boundary_output_iou_coef: 0.6684 - val_loss: 0.2224 - val_mask_output_loss: 0.1248 - val_boundary_output_loss: 0.1952 - val_mask_output_iou_coef: 0.8656 - val_boundary_output_iou_coef: 0.7645\n", "Epoch 95/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3316 - mask_output_loss: 0.1947 - boundary_output_loss: 0.2739 - mask_output_iou_coef: 0.8021 - boundary_output_iou_coef: 0.6752\n", "Epoch 95: val_loss did not improve from 0.21731\n", "230/230 [==============================] - 528s 2s/step - loss: 0.3316 - mask_output_loss: 0.1947 - boundary_output_loss: 0.2739 - mask_output_iou_coef: 0.8021 - boundary_output_iou_coef: 0.6752 - val_loss: 0.2229 - val_mask_output_loss: 0.1249 - val_boundary_output_loss: 0.1961 - val_mask_output_iou_coef: 0.8678 - val_boundary_output_iou_coef: 0.7680\n", "Epoch 96/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3204 - mask_output_loss: 0.1861 - boundary_output_loss: 0.2686 - mask_output_iou_coef: 0.8090 - boundary_output_iou_coef: 0.6806\n", "Epoch 96: val_loss improved from 0.21731 to 0.21675, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 525s 2s/step - loss: 0.3204 - mask_output_loss: 0.1861 - boundary_output_loss: 0.2686 - mask_output_iou_coef: 0.8090 - boundary_output_iou_coef: 0.6806 - val_loss: 0.2168 - val_mask_output_loss: 0.1215 - val_boundary_output_loss: 0.1904 - val_mask_output_iou_coef: 0.8727 - val_boundary_output_iou_coef: 0.7723\n", "Epoch 97/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3203 - mask_output_loss: 0.1849 - boundary_output_loss: 0.2707 - mask_output_iou_coef: 0.8087 - boundary_output_iou_coef: 0.6789\n", "Epoch 97: val_loss did not improve from 0.21675\n", "230/230 [==============================] - 524s 2s/step - loss: 0.3203 - mask_output_loss: 0.1849 - boundary_output_loss: 0.2707 - mask_output_iou_coef: 0.8087 - boundary_output_iou_coef: 0.6789 - val_loss: 0.2222 - val_mask_output_loss: 0.1244 - val_boundary_output_loss: 0.1955 - val_mask_output_iou_coef: 0.8748 - val_boundary_output_iou_coef: 0.7678\n", "Epoch 98/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3256 - mask_output_loss: 0.1901 - boundary_output_loss: 0.2710 - mask_output_iou_coef: 0.8068 - boundary_output_iou_coef: 0.6795\n", "Epoch 98: val_loss did not improve from 0.21675\n", "230/230 [==============================] - 523s 2s/step - loss: 0.3256 - mask_output_loss: 0.1901 - boundary_output_loss: 0.2710 - mask_output_iou_coef: 0.8068 - boundary_output_iou_coef: 0.6795 - val_loss: 0.2270 - val_mask_output_loss: 0.1270 - val_boundary_output_loss: 0.2000 - val_mask_output_iou_coef: 0.8640 - val_boundary_output_iou_coef: 0.7604\n", "Epoch 99/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3245 - mask_output_loss: 0.1894 - boundary_output_loss: 0.2700 - mask_output_iou_coef: 0.8068 - boundary_output_iou_coef: 0.6800\n", "Epoch 99: val_loss did not improve from 0.21675\n", "230/230 [==============================] - 523s 2s/step - loss: 0.3245 - mask_output_loss: 0.1894 - boundary_output_loss: 0.2700 - mask_output_iou_coef: 0.8068 - boundary_output_iou_coef: 0.6800 - val_loss: 0.2213 - val_mask_output_loss: 0.1251 - val_boundary_output_loss: 0.1925 - val_mask_output_iou_coef: 0.8687 - val_boundary_output_iou_coef: 0.7705\n", "Epoch 100/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3318 - mask_output_loss: 0.1958 - boundary_output_loss: 0.2721 - mask_output_iou_coef: 0.8006 - boundary_output_iou_coef: 0.6784\n", "Epoch 100: val_loss did not improve from 0.21675\n", "230/230 [==============================] - 525s 2s/step - loss: 0.3318 - mask_output_loss: 0.1958 - boundary_output_loss: 0.2721 - mask_output_iou_coef: 0.8006 - boundary_output_iou_coef: 0.6784 - val_loss: 0.2203 - val_mask_output_loss: 0.1236 - val_boundary_output_loss: 0.1934 - val_mask_output_iou_coef: 0.8678 - val_boundary_output_iou_coef: 0.7704\n", "Epoch 101/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3206 - mask_output_loss: 0.1840 - boundary_output_loss: 0.2732 - mask_output_iou_coef: 0.8102 - boundary_output_iou_coef: 0.6763\n", "Epoch 101: val_loss did not improve from 0.21675\n", "230/230 [==============================] - 535s 2s/step - loss: 0.3206 - mask_output_loss: 0.1840 - boundary_output_loss: 0.2732 - mask_output_iou_coef: 0.8102 - boundary_output_iou_coef: 0.6763 - val_loss: 0.2168 - val_mask_output_loss: 0.1196 - val_boundary_output_loss: 0.1943 - val_mask_output_iou_coef: 0.8722 - val_boundary_output_iou_coef: 0.7671\n", "Epoch 102/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3188 - mask_output_loss: 0.1853 - boundary_output_loss: 0.2671 - mask_output_iou_coef: 0.8114 - boundary_output_iou_coef: 0.6828\n", "Epoch 102: val_loss improved from 0.21675 to 0.21462, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 537s 2s/step - loss: 0.3188 - mask_output_loss: 0.1853 - boundary_output_loss: 0.2671 - mask_output_iou_coef: 0.8114 - boundary_output_iou_coef: 0.6828 - val_loss: 0.2146 - val_mask_output_loss: 0.1191 - val_boundary_output_loss: 0.1911 - val_mask_output_iou_coef: 0.8717 - val_boundary_output_iou_coef: 0.7704\n", "Epoch 103/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3292 - mask_output_loss: 0.1932 - boundary_output_loss: 0.2719 - mask_output_iou_coef: 0.8031 - boundary_output_iou_coef: 0.6782\n", "Epoch 103: val_loss did not improve from 0.21462\n", "230/230 [==============================] - 525s 2s/step - loss: 0.3292 - mask_output_loss: 0.1932 - boundary_output_loss: 0.2719 - mask_output_iou_coef: 0.8031 - boundary_output_iou_coef: 0.6782 - val_loss: 0.2226 - val_mask_output_loss: 0.1248 - val_boundary_output_loss: 0.1955 - val_mask_output_iou_coef: 0.8720 - val_boundary_output_iou_coef: 0.7690\n", "Epoch 104/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3254 - mask_output_loss: 0.1901 - boundary_output_loss: 0.2705 - mask_output_iou_coef: 0.8059 - boundary_output_iou_coef: 0.6790\n", "Epoch 104: val_loss did not improve from 0.21462\n", "230/230 [==============================] - 535s 2s/step - loss: 0.3254 - mask_output_loss: 0.1901 - boundary_output_loss: 0.2705 - mask_output_iou_coef: 0.8059 - boundary_output_iou_coef: 0.6790 - val_loss: 0.2174 - val_mask_output_loss: 0.1210 - val_boundary_output_loss: 0.1929 - val_mask_output_iou_coef: 0.8715 - val_boundary_output_iou_coef: 0.7681\n", "Epoch 105/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3363 - mask_output_loss: 0.1934 - boundary_output_loss: 0.2858 - mask_output_iou_coef: 0.8031 - boundary_output_iou_coef: 0.6654\n", "Epoch 105: val_loss did not improve from 0.21462\n", "230/230 [==============================] - 531s 2s/step - loss: 0.3363 - mask_output_loss: 0.1934 - boundary_output_loss: 0.2858 - mask_output_iou_coef: 0.8031 - boundary_output_iou_coef: 0.6654 - val_loss: 0.2260 - val_mask_output_loss: 0.1263 - val_boundary_output_loss: 0.1995 - val_mask_output_iou_coef: 0.8707 - val_boundary_output_iou_coef: 0.7645\n", "Epoch 106/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3387 - mask_output_loss: 0.1987 - boundary_output_loss: 0.2800 - mask_output_iou_coef: 0.8000 - boundary_output_iou_coef: 0.6699\n", "Epoch 106: val_loss did not improve from 0.21462\n", "230/230 [==============================] - 514s 2s/step - loss: 0.3387 - mask_output_loss: 0.1987 - boundary_output_loss: 0.2800 - mask_output_iou_coef: 0.8000 - boundary_output_iou_coef: 0.6699 - val_loss: 0.2204 - val_mask_output_loss: 0.1223 - val_boundary_output_loss: 0.1962 - val_mask_output_iou_coef: 0.8702 - val_boundary_output_iou_coef: 0.7654\n", "Epoch 107/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3166 - mask_output_loss: 0.1826 - boundary_output_loss: 0.2679 - mask_output_iou_coef: 0.8121 - boundary_output_iou_coef: 0.6818\n", "Epoch 107: val_loss improved from 0.21462 to 0.21432, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 533s 2s/step - loss: 0.3166 - mask_output_loss: 0.1826 - boundary_output_loss: 0.2679 - mask_output_iou_coef: 0.8121 - boundary_output_iou_coef: 0.6818 - val_loss: 0.2143 - val_mask_output_loss: 0.1183 - val_boundary_output_loss: 0.1920 - val_mask_output_iou_coef: 0.8762 - val_boundary_output_iou_coef: 0.7694\n", "Epoch 108/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3211 - mask_output_loss: 0.1853 - boundary_output_loss: 0.2716 - mask_output_iou_coef: 0.8125 - boundary_output_iou_coef: 0.6784\n", "Epoch 108: val_loss did not improve from 0.21432\n", "230/230 [==============================] - 538s 2s/step - loss: 0.3211 - mask_output_loss: 0.1853 - boundary_output_loss: 0.2716 - mask_output_iou_coef: 0.8125 - boundary_output_iou_coef: 0.6784 - val_loss: 0.2184 - val_mask_output_loss: 0.1214 - val_boundary_output_loss: 0.1941 - val_mask_output_iou_coef: 0.8735 - val_boundary_output_iou_coef: 0.7683\n", "Epoch 109/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3073 - mask_output_loss: 0.1741 - boundary_output_loss: 0.2665 - mask_output_iou_coef: 0.8181 - boundary_output_iou_coef: 0.6828\n", "Epoch 109: val_loss did not improve from 0.21432\n", "230/230 [==============================] - 532s 2s/step - loss: 0.3073 - mask_output_loss: 0.1741 - boundary_output_loss: 0.2665 - mask_output_iou_coef: 0.8181 - boundary_output_iou_coef: 0.6828 - val_loss: 0.2174 - val_mask_output_loss: 0.1230 - val_boundary_output_loss: 0.1889 - val_mask_output_iou_coef: 0.8692 - val_boundary_output_iou_coef: 0.7727\n", "Epoch 110/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3221 - mask_output_loss: 0.1865 - boundary_output_loss: 0.2713 - mask_output_iou_coef: 0.8082 - boundary_output_iou_coef: 0.6783\n", "Epoch 110: val_loss did not improve from 0.21432\n", "230/230 [==============================] - 534s 2s/step - loss: 0.3221 - mask_output_loss: 0.1865 - boundary_output_loss: 0.2713 - mask_output_iou_coef: 0.8082 - boundary_output_iou_coef: 0.6783 - val_loss: 0.2175 - val_mask_output_loss: 0.1213 - val_boundary_output_loss: 0.1923 - val_mask_output_iou_coef: 0.8720 - val_boundary_output_iou_coef: 0.7688\n", "Epoch 111/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3139 - mask_output_loss: 0.1830 - boundary_output_loss: 0.2618 - mask_output_iou_coef: 0.8114 - boundary_output_iou_coef: 0.6881\n", "Epoch 111: val_loss improved from 0.21432 to 0.21359, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 530s 2s/step - loss: 0.3139 - mask_output_loss: 0.1830 - boundary_output_loss: 0.2618 - mask_output_iou_coef: 0.8114 - boundary_output_iou_coef: 0.6881 - val_loss: 0.2136 - val_mask_output_loss: 0.1203 - val_boundary_output_loss: 0.1866 - val_mask_output_iou_coef: 0.8712 - val_boundary_output_iou_coef: 0.7760\n", "Epoch 112/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3156 - mask_output_loss: 0.1837 - boundary_output_loss: 0.2639 - mask_output_iou_coef: 0.8122 - boundary_output_iou_coef: 0.6858\n", "Epoch 112: val_loss did not improve from 0.21359\n", "230/230 [==============================] - 533s 2s/step - loss: 0.3156 - mask_output_loss: 0.1837 - boundary_output_loss: 0.2639 - mask_output_iou_coef: 0.8122 - boundary_output_iou_coef: 0.6858 - val_loss: 0.2167 - val_mask_output_loss: 0.1212 - val_boundary_output_loss: 0.1910 - val_mask_output_iou_coef: 0.8715 - val_boundary_output_iou_coef: 0.7708\n", "Epoch 113/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3281 - mask_output_loss: 0.1940 - boundary_output_loss: 0.2682 - mask_output_iou_coef: 0.8040 - boundary_output_iou_coef: 0.6814\n", "Epoch 113: val_loss improved from 0.21359 to 0.21032, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 528s 2s/step - loss: 0.3281 - mask_output_loss: 0.1940 - boundary_output_loss: 0.2682 - mask_output_iou_coef: 0.8040 - boundary_output_iou_coef: 0.6814 - val_loss: 0.2103 - val_mask_output_loss: 0.1166 - val_boundary_output_loss: 0.1875 - val_mask_output_iou_coef: 0.8737 - val_boundary_output_iou_coef: 0.7752\n", "Epoch 114/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3237 - mask_output_loss: 0.1896 - boundary_output_loss: 0.2681 - mask_output_iou_coef: 0.8077 - boundary_output_iou_coef: 0.6821\n", "Epoch 114: val_loss did not improve from 0.21032\n", "230/230 [==============================] - 550s 2s/step - loss: 0.3237 - mask_output_loss: 0.1896 - boundary_output_loss: 0.2681 - mask_output_iou_coef: 0.8077 - boundary_output_iou_coef: 0.6821 - val_loss: 0.2142 - val_mask_output_loss: 0.1188 - val_boundary_output_loss: 0.1906 - val_mask_output_iou_coef: 0.8734 - val_boundary_output_iou_coef: 0.7694\n", "Epoch 115/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3409 - mask_output_loss: 0.1971 - boundary_output_loss: 0.2876 - mask_output_iou_coef: 0.7993 - boundary_output_iou_coef: 0.6637\n", "Epoch 115: val_loss did not improve from 0.21032\n", "230/230 [==============================] - 562s 2s/step - loss: 0.3409 - mask_output_loss: 0.1971 - boundary_output_loss: 0.2876 - mask_output_iou_coef: 0.7993 - boundary_output_iou_coef: 0.6637 - val_loss: 0.2187 - val_mask_output_loss: 0.1213 - val_boundary_output_loss: 0.1947 - val_mask_output_iou_coef: 0.8710 - val_boundary_output_iou_coef: 0.7644\n", "Epoch 116/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3319 - mask_output_loss: 0.1952 - boundary_output_loss: 0.2734 - mask_output_iou_coef: 0.8027 - boundary_output_iou_coef: 0.6765\n", "Epoch 116: val_loss did not improve from 0.21032\n", "230/230 [==============================] - 546s 2s/step - loss: 0.3319 - mask_output_loss: 0.1952 - boundary_output_loss: 0.2734 - mask_output_iou_coef: 0.8027 - boundary_output_iou_coef: 0.6765 - val_loss: 0.2258 - val_mask_output_loss: 0.1256 - val_boundary_output_loss: 0.2005 - val_mask_output_iou_coef: 0.8705 - val_boundary_output_iou_coef: 0.7657\n", "Epoch 117/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3210 - mask_output_loss: 0.1839 - boundary_output_loss: 0.2742 - mask_output_iou_coef: 0.8095 - boundary_output_iou_coef: 0.6753\n", "Epoch 117: val_loss did not improve from 0.21032\n", "230/230 [==============================] - 541s 2s/step - loss: 0.3210 - mask_output_loss: 0.1839 - boundary_output_loss: 0.2742 - mask_output_iou_coef: 0.8095 - boundary_output_iou_coef: 0.6753 - val_loss: 0.2273 - val_mask_output_loss: 0.1267 - val_boundary_output_loss: 0.2010 - val_mask_output_iou_coef: 0.8684 - val_boundary_output_iou_coef: 0.7612\n", "Epoch 118/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3186 - mask_output_loss: 0.1846 - boundary_output_loss: 0.2679 - mask_output_iou_coef: 0.8094 - boundary_output_iou_coef: 0.6816\n", "Epoch 118: val_loss did not improve from 0.21032\n", "230/230 [==============================] - 537s 2s/step - loss: 0.3186 - mask_output_loss: 0.1846 - boundary_output_loss: 0.2679 - mask_output_iou_coef: 0.8094 - boundary_output_iou_coef: 0.6816 - val_loss: 0.2158 - val_mask_output_loss: 0.1203 - val_boundary_output_loss: 0.1910 - val_mask_output_iou_coef: 0.8749 - val_boundary_output_iou_coef: 0.7712\n", "Epoch 119/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3195 - mask_output_loss: 0.1862 - boundary_output_loss: 0.2666 - mask_output_iou_coef: 0.8099 - boundary_output_iou_coef: 0.6834\n", "Epoch 119: val_loss did not improve from 0.21032\n", "230/230 [==============================] - 529s 2s/step - loss: 0.3195 - mask_output_loss: 0.1862 - boundary_output_loss: 0.2666 - mask_output_iou_coef: 0.8099 - boundary_output_iou_coef: 0.6834 - val_loss: 0.2156 - val_mask_output_loss: 0.1202 - val_boundary_output_loss: 0.1908 - val_mask_output_iou_coef: 0.8727 - val_boundary_output_iou_coef: 0.7721\n", "Epoch 120/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3100 - mask_output_loss: 0.1789 - boundary_output_loss: 0.2622 - mask_output_iou_coef: 0.8158 - boundary_output_iou_coef: 0.6876\n", "Epoch 120: val_loss did not improve from 0.21032\n", "230/230 [==============================] - 538s 2s/step - loss: 0.3100 - mask_output_loss: 0.1789 - boundary_output_loss: 0.2622 - mask_output_iou_coef: 0.8158 - boundary_output_iou_coef: 0.6876 - val_loss: 0.2157 - val_mask_output_loss: 0.1207 - val_boundary_output_loss: 0.1900 - val_mask_output_iou_coef: 0.8721 - val_boundary_output_iou_coef: 0.7709\n", "Epoch 121/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3177 - mask_output_loss: 0.1857 - boundary_output_loss: 0.2639 - mask_output_iou_coef: 0.8098 - boundary_output_iou_coef: 0.6859\n", "Epoch 121: val_loss did not improve from 0.21032\n", "230/230 [==============================] - 515s 2s/step - loss: 0.3177 - mask_output_loss: 0.1857 - boundary_output_loss: 0.2639 - mask_output_iou_coef: 0.8098 - boundary_output_iou_coef: 0.6859 - val_loss: 0.2186 - val_mask_output_loss: 0.1219 - val_boundary_output_loss: 0.1935 - val_mask_output_iou_coef: 0.8709 - val_boundary_output_iou_coef: 0.7664\n", "Epoch 122/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3035 - mask_output_loss: 0.1722 - boundary_output_loss: 0.2625 - mask_output_iou_coef: 0.8206 - boundary_output_iou_coef: 0.6870\n", "Epoch 122: val_loss did not improve from 0.21032\n", "230/230 [==============================] - 534s 2s/step - loss: 0.3035 - mask_output_loss: 0.1722 - boundary_output_loss: 0.2625 - mask_output_iou_coef: 0.8206 - boundary_output_iou_coef: 0.6870 - val_loss: 0.2167 - val_mask_output_loss: 0.1208 - val_boundary_output_loss: 0.1919 - val_mask_output_iou_coef: 0.8748 - val_boundary_output_iou_coef: 0.7706\n", "Epoch 123/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3181 - mask_output_loss: 0.1859 - boundary_output_loss: 0.2645 - mask_output_iou_coef: 0.8087 - boundary_output_iou_coef: 0.6859\n", "Epoch 123: val_loss did not improve from 0.21032\n", "230/230 [==============================] - 543s 2s/step - loss: 0.3181 - mask_output_loss: 0.1859 - boundary_output_loss: 0.2645 - mask_output_iou_coef: 0.8087 - boundary_output_iou_coef: 0.6859 - val_loss: 0.2146 - val_mask_output_loss: 0.1194 - val_boundary_output_loss: 0.1904 - val_mask_output_iou_coef: 0.8768 - val_boundary_output_iou_coef: 0.7737\n", "Epoch 124/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3118 - mask_output_loss: 0.1831 - boundary_output_loss: 0.2575 - mask_output_iou_coef: 0.8134 - boundary_output_iou_coef: 0.6931\n", "Epoch 124: val_loss improved from 0.21032 to 0.20893, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 543s 2s/step - loss: 0.3118 - mask_output_loss: 0.1831 - boundary_output_loss: 0.2575 - mask_output_iou_coef: 0.8134 - boundary_output_iou_coef: 0.6931 - val_loss: 0.2089 - val_mask_output_loss: 0.1161 - val_boundary_output_loss: 0.1856 - val_mask_output_iou_coef: 0.8754 - val_boundary_output_iou_coef: 0.7769\n", "Epoch 125/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3098 - mask_output_loss: 0.1786 - boundary_output_loss: 0.2624 - mask_output_iou_coef: 0.8160 - boundary_output_iou_coef: 0.6870\n", "Epoch 125: val_loss did not improve from 0.20893\n", "230/230 [==============================] - 558s 2s/step - loss: 0.3098 - mask_output_loss: 0.1786 - boundary_output_loss: 0.2624 - mask_output_iou_coef: 0.8160 - boundary_output_iou_coef: 0.6870 - val_loss: 0.2145 - val_mask_output_loss: 0.1191 - val_boundary_output_loss: 0.1906 - val_mask_output_iou_coef: 0.8752 - val_boundary_output_iou_coef: 0.7719\n", "Epoch 126/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3166 - mask_output_loss: 0.1830 - boundary_output_loss: 0.2670 - mask_output_iou_coef: 0.8122 - boundary_output_iou_coef: 0.6823\n", "Epoch 126: val_loss did not improve from 0.20893\n", "230/230 [==============================] - 547s 2s/step - loss: 0.3166 - mask_output_loss: 0.1830 - boundary_output_loss: 0.2670 - mask_output_iou_coef: 0.8122 - boundary_output_iou_coef: 0.6823 - val_loss: 0.2163 - val_mask_output_loss: 0.1212 - val_boundary_output_loss: 0.1902 - val_mask_output_iou_coef: 0.8752 - val_boundary_output_iou_coef: 0.7745\n", "Epoch 127/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3167 - mask_output_loss: 0.1837 - boundary_output_loss: 0.2660 - mask_output_iou_coef: 0.8116 - boundary_output_iou_coef: 0.6842\n", "Epoch 127: val_loss did not improve from 0.20893\n", "230/230 [==============================] - 546s 2s/step - loss: 0.3167 - mask_output_loss: 0.1837 - boundary_output_loss: 0.2660 - mask_output_iou_coef: 0.8116 - boundary_output_iou_coef: 0.6842 - val_loss: 0.2127 - val_mask_output_loss: 0.1185 - val_boundary_output_loss: 0.1884 - val_mask_output_iou_coef: 0.8772 - val_boundary_output_iou_coef: 0.7738\n", "Epoch 128/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3197 - mask_output_loss: 0.1867 - boundary_output_loss: 0.2659 - mask_output_iou_coef: 0.8096 - boundary_output_iou_coef: 0.6838\n", "Epoch 128: val_loss did not improve from 0.20893\n", "230/230 [==============================] - 545s 2s/step - loss: 0.3197 - mask_output_loss: 0.1867 - boundary_output_loss: 0.2659 - mask_output_iou_coef: 0.8096 - boundary_output_iou_coef: 0.6838 - val_loss: 0.2129 - val_mask_output_loss: 0.1180 - val_boundary_output_loss: 0.1899 - val_mask_output_iou_coef: 0.8726 - val_boundary_output_iou_coef: 0.7730\n", "Epoch 129/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3144 - mask_output_loss: 0.1821 - boundary_output_loss: 0.2645 - mask_output_iou_coef: 0.8132 - boundary_output_iou_coef: 0.6855\n", "Epoch 129: val_loss did not improve from 0.20893\n", "230/230 [==============================] - 536s 2s/step - loss: 0.3144 - mask_output_loss: 0.1821 - boundary_output_loss: 0.2645 - mask_output_iou_coef: 0.8132 - boundary_output_iou_coef: 0.6855 - val_loss: 0.2127 - val_mask_output_loss: 0.1199 - val_boundary_output_loss: 0.1855 - val_mask_output_iou_coef: 0.8762 - val_boundary_output_iou_coef: 0.7770\n", "Epoch 130/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3132 - mask_output_loss: 0.1812 - boundary_output_loss: 0.2640 - mask_output_iou_coef: 0.8140 - boundary_output_iou_coef: 0.6856\n", "Epoch 130: val_loss did not improve from 0.20893\n", "230/230 [==============================] - 558s 2s/step - loss: 0.3132 - mask_output_loss: 0.1812 - boundary_output_loss: 0.2640 - mask_output_iou_coef: 0.8140 - boundary_output_iou_coef: 0.6856 - val_loss: 0.2136 - val_mask_output_loss: 0.1197 - val_boundary_output_loss: 0.1878 - val_mask_output_iou_coef: 0.8730 - val_boundary_output_iou_coef: 0.7755\n", "Epoch 131/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3216 - mask_output_loss: 0.1871 - boundary_output_loss: 0.2690 - mask_output_iou_coef: 0.8088 - boundary_output_iou_coef: 0.6810\n", "Epoch 131: val_loss did not improve from 0.20893\n", "230/230 [==============================] - 564s 2s/step - loss: 0.3216 - mask_output_loss: 0.1871 - boundary_output_loss: 0.2690 - mask_output_iou_coef: 0.8088 - boundary_output_iou_coef: 0.6810 - val_loss: 0.2138 - val_mask_output_loss: 0.1197 - val_boundary_output_loss: 0.1881 - val_mask_output_iou_coef: 0.8772 - val_boundary_output_iou_coef: 0.7743\n", "Epoch 132/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3123 - mask_output_loss: 0.1801 - boundary_output_loss: 0.2644 - mask_output_iou_coef: 0.8135 - boundary_output_iou_coef: 0.6850\n", "Epoch 132: val_loss did not improve from 0.20893\n", "230/230 [==============================] - 551s 2s/step - loss: 0.3123 - mask_output_loss: 0.1801 - boundary_output_loss: 0.2644 - mask_output_iou_coef: 0.8135 - boundary_output_iou_coef: 0.6850 - val_loss: 0.2116 - val_mask_output_loss: 0.1176 - val_boundary_output_loss: 0.1880 - val_mask_output_iou_coef: 0.8757 - val_boundary_output_iou_coef: 0.7734\n", "Epoch 133/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3227 - mask_output_loss: 0.1883 - boundary_output_loss: 0.2687 - mask_output_iou_coef: 0.8073 - boundary_output_iou_coef: 0.6811\n", "Epoch 133: val_loss did not improve from 0.20893\n", "230/230 [==============================] - 561s 2s/step - loss: 0.3227 - mask_output_loss: 0.1883 - boundary_output_loss: 0.2687 - mask_output_iou_coef: 0.8073 - boundary_output_iou_coef: 0.6811 - val_loss: 0.2122 - val_mask_output_loss: 0.1182 - val_boundary_output_loss: 0.1880 - val_mask_output_iou_coef: 0.8773 - val_boundary_output_iou_coef: 0.7759\n", "Epoch 134/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3178 - mask_output_loss: 0.1853 - boundary_output_loss: 0.2649 - mask_output_iou_coef: 0.8126 - boundary_output_iou_coef: 0.6854\n", "Epoch 134: val_loss did not improve from 0.20893\n", "230/230 [==============================] - 564s 2s/step - loss: 0.3178 - mask_output_loss: 0.1853 - boundary_output_loss: 0.2649 - mask_output_iou_coef: 0.8126 - boundary_output_iou_coef: 0.6854 - val_loss: 0.2114 - val_mask_output_loss: 0.1172 - val_boundary_output_loss: 0.1884 - val_mask_output_iou_coef: 0.8771 - val_boundary_output_iou_coef: 0.7743\n", "Epoch 135/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3063 - mask_output_loss: 0.1760 - boundary_output_loss: 0.2606 - mask_output_iou_coef: 0.8189 - boundary_output_iou_coef: 0.6897\n", "Epoch 135: val_loss improved from 0.20893 to 0.20589, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 524s 2s/step - loss: 0.3063 - mask_output_loss: 0.1760 - boundary_output_loss: 0.2606 - mask_output_iou_coef: 0.8189 - boundary_output_iou_coef: 0.6897 - val_loss: 0.2059 - val_mask_output_loss: 0.1146 - val_boundary_output_loss: 0.1825 - val_mask_output_iou_coef: 0.8772 - val_boundary_output_iou_coef: 0.7787\n", "Epoch 136/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3154 - mask_output_loss: 0.1828 - boundary_output_loss: 0.2652 - mask_output_iou_coef: 0.8131 - boundary_output_iou_coef: 0.6852\n", "Epoch 136: val_loss did not improve from 0.20589\n", "230/230 [==============================] - 518s 2s/step - loss: 0.3154 - mask_output_loss: 0.1828 - boundary_output_loss: 0.2652 - mask_output_iou_coef: 0.8131 - boundary_output_iou_coef: 0.6852 - val_loss: 0.2080 - val_mask_output_loss: 0.1149 - val_boundary_output_loss: 0.1863 - val_mask_output_iou_coef: 0.8806 - val_boundary_output_iou_coef: 0.7779\n", "Epoch 137/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3184 - mask_output_loss: 0.1855 - boundary_output_loss: 0.2657 - mask_output_iou_coef: 0.8102 - boundary_output_iou_coef: 0.6835\n", "Epoch 137: val_loss improved from 0.20589 to 0.20411, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 541s 2s/step - loss: 0.3184 - mask_output_loss: 0.1855 - boundary_output_loss: 0.2657 - mask_output_iou_coef: 0.8102 - boundary_output_iou_coef: 0.6835 - val_loss: 0.2041 - val_mask_output_loss: 0.1125 - val_boundary_output_loss: 0.1832 - val_mask_output_iou_coef: 0.8810 - val_boundary_output_iou_coef: 0.7801\n", "Epoch 138/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3108 - mask_output_loss: 0.1805 - boundary_output_loss: 0.2607 - mask_output_iou_coef: 0.8142 - boundary_output_iou_coef: 0.6892\n", "Epoch 138: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 535s 2s/step - loss: 0.3108 - mask_output_loss: 0.1805 - boundary_output_loss: 0.2607 - mask_output_iou_coef: 0.8142 - boundary_output_iou_coef: 0.6892 - val_loss: 0.2107 - val_mask_output_loss: 0.1173 - val_boundary_output_loss: 0.1869 - val_mask_output_iou_coef: 0.8751 - val_boundary_output_iou_coef: 0.7762\n", "Epoch 139/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3127 - mask_output_loss: 0.1824 - boundary_output_loss: 0.2605 - mask_output_iou_coef: 0.8144 - boundary_output_iou_coef: 0.6902\n", "Epoch 139: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 528s 2s/step - loss: 0.3127 - mask_output_loss: 0.1824 - boundary_output_loss: 0.2605 - mask_output_iou_coef: 0.8144 - boundary_output_iou_coef: 0.6902 - val_loss: 0.2111 - val_mask_output_loss: 0.1163 - val_boundary_output_loss: 0.1895 - val_mask_output_iou_coef: 0.8761 - val_boundary_output_iou_coef: 0.7735\n", "Epoch 140/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3136 - mask_output_loss: 0.1814 - boundary_output_loss: 0.2645 - mask_output_iou_coef: 0.8130 - boundary_output_iou_coef: 0.6858\n", "Epoch 140: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 532s 2s/step - loss: 0.3136 - mask_output_loss: 0.1814 - boundary_output_loss: 0.2645 - mask_output_iou_coef: 0.8130 - boundary_output_iou_coef: 0.6858 - val_loss: 0.2133 - val_mask_output_loss: 0.1183 - val_boundary_output_loss: 0.1898 - val_mask_output_iou_coef: 0.8758 - val_boundary_output_iou_coef: 0.7726\n", "Epoch 141/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3142 - mask_output_loss: 0.1818 - boundary_output_loss: 0.2647 - mask_output_iou_coef: 0.8151 - boundary_output_iou_coef: 0.6851\n", "Epoch 141: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 537s 2s/step - loss: 0.3142 - mask_output_loss: 0.1818 - boundary_output_loss: 0.2647 - mask_output_iou_coef: 0.8151 - boundary_output_iou_coef: 0.6851 - val_loss: 0.2101 - val_mask_output_loss: 0.1173 - val_boundary_output_loss: 0.1857 - val_mask_output_iou_coef: 0.8764 - val_boundary_output_iou_coef: 0.7795\n", "Epoch 142/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3213 - mask_output_loss: 0.1872 - boundary_output_loss: 0.2682 - mask_output_iou_coef: 0.8085 - boundary_output_iou_coef: 0.6817\n", "Epoch 142: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 535s 2s/step - loss: 0.3213 - mask_output_loss: 0.1872 - boundary_output_loss: 0.2682 - mask_output_iou_coef: 0.8085 - boundary_output_iou_coef: 0.6817 - val_loss: 0.2138 - val_mask_output_loss: 0.1196 - val_boundary_output_loss: 0.1885 - val_mask_output_iou_coef: 0.8693 - val_boundary_output_iou_coef: 0.7730\n", "Epoch 143/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3068 - mask_output_loss: 0.1783 - boundary_output_loss: 0.2568 - mask_output_iou_coef: 0.8158 - boundary_output_iou_coef: 0.6940\n", "Epoch 143: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 540s 2s/step - loss: 0.3068 - mask_output_loss: 0.1783 - boundary_output_loss: 0.2568 - mask_output_iou_coef: 0.8158 - boundary_output_iou_coef: 0.6940 - val_loss: 0.2071 - val_mask_output_loss: 0.1149 - val_boundary_output_loss: 0.1845 - val_mask_output_iou_coef: 0.8807 - val_boundary_output_iou_coef: 0.7787\n", "Epoch 144/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3132 - mask_output_loss: 0.1822 - boundary_output_loss: 0.2620 - mask_output_iou_coef: 0.8159 - boundary_output_iou_coef: 0.6884\n", "Epoch 144: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 539s 2s/step - loss: 0.3132 - mask_output_loss: 0.1822 - boundary_output_loss: 0.2620 - mask_output_iou_coef: 0.8159 - boundary_output_iou_coef: 0.6884 - val_loss: 0.2141 - val_mask_output_loss: 0.1192 - val_boundary_output_loss: 0.1899 - val_mask_output_iou_coef: 0.8762 - val_boundary_output_iou_coef: 0.7762\n", "Epoch 145/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3109 - mask_output_loss: 0.1796 - boundary_output_loss: 0.2627 - mask_output_iou_coef: 0.8150 - boundary_output_iou_coef: 0.6877\n", "Epoch 145: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 540s 2s/step - loss: 0.3109 - mask_output_loss: 0.1796 - boundary_output_loss: 0.2627 - mask_output_iou_coef: 0.8150 - boundary_output_iou_coef: 0.6877 - val_loss: 0.2116 - val_mask_output_loss: 0.1163 - val_boundary_output_loss: 0.1906 - val_mask_output_iou_coef: 0.8775 - val_boundary_output_iou_coef: 0.7713\n", "Epoch 146/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3131 - mask_output_loss: 0.1826 - boundary_output_loss: 0.2611 - mask_output_iou_coef: 0.8138 - boundary_output_iou_coef: 0.6889\n", "Epoch 146: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 544s 2s/step - loss: 0.3131 - mask_output_loss: 0.1826 - boundary_output_loss: 0.2611 - mask_output_iou_coef: 0.8138 - boundary_output_iou_coef: 0.6889 - val_loss: 0.2158 - val_mask_output_loss: 0.1202 - val_boundary_output_loss: 0.1912 - val_mask_output_iou_coef: 0.8733 - val_boundary_output_iou_coef: 0.7719\n", "Epoch 147/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3096 - mask_output_loss: 0.1800 - boundary_output_loss: 0.2593 - mask_output_iou_coef: 0.8137 - boundary_output_iou_coef: 0.6903\n", "Epoch 147: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 560s 2s/step - loss: 0.3096 - mask_output_loss: 0.1800 - boundary_output_loss: 0.2593 - mask_output_iou_coef: 0.8137 - boundary_output_iou_coef: 0.6903 - val_loss: 0.2161 - val_mask_output_loss: 0.1200 - val_boundary_output_loss: 0.1922 - val_mask_output_iou_coef: 0.8751 - val_boundary_output_iou_coef: 0.7736\n", "Epoch 148/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3180 - mask_output_loss: 0.1866 - boundary_output_loss: 0.2628 - mask_output_iou_coef: 0.8116 - boundary_output_iou_coef: 0.6882\n", "Epoch 148: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 569s 2s/step - loss: 0.3180 - mask_output_loss: 0.1866 - boundary_output_loss: 0.2628 - mask_output_iou_coef: 0.8116 - boundary_output_iou_coef: 0.6882 - val_loss: 0.2117 - val_mask_output_loss: 0.1175 - val_boundary_output_loss: 0.1885 - val_mask_output_iou_coef: 0.8768 - val_boundary_output_iou_coef: 0.7764\n", "Epoch 149/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3114 - mask_output_loss: 0.1808 - boundary_output_loss: 0.2612 - mask_output_iou_coef: 0.8150 - boundary_output_iou_coef: 0.6885\n", "Epoch 149: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 561s 2s/step - loss: 0.3114 - mask_output_loss: 0.1808 - boundary_output_loss: 0.2612 - mask_output_iou_coef: 0.8150 - boundary_output_iou_coef: 0.6885 - val_loss: 0.2208 - val_mask_output_loss: 0.1230 - val_boundary_output_loss: 0.1957 - val_mask_output_iou_coef: 0.8807 - val_boundary_output_iou_coef: 0.7739\n", "Epoch 150/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3153 - mask_output_loss: 0.1821 - boundary_output_loss: 0.2664 - mask_output_iou_coef: 0.8149 - boundary_output_iou_coef: 0.6849\n", "Epoch 150: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 563s 2s/step - loss: 0.3153 - mask_output_loss: 0.1821 - boundary_output_loss: 0.2664 - mask_output_iou_coef: 0.8149 - boundary_output_iou_coef: 0.6849 - val_loss: 0.2127 - val_mask_output_loss: 0.1186 - val_boundary_output_loss: 0.1882 - val_mask_output_iou_coef: 0.8785 - val_boundary_output_iou_coef: 0.7802\n", "Epoch 151/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3063 - mask_output_loss: 0.1771 - boundary_output_loss: 0.2583 - mask_output_iou_coef: 0.8176 - boundary_output_iou_coef: 0.6918\n", "Epoch 151: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 566s 2s/step - loss: 0.3063 - mask_output_loss: 0.1771 - boundary_output_loss: 0.2583 - mask_output_iou_coef: 0.8176 - boundary_output_iou_coef: 0.6918 - val_loss: 0.2063 - val_mask_output_loss: 0.1141 - val_boundary_output_loss: 0.1843 - val_mask_output_iou_coef: 0.8799 - val_boundary_output_iou_coef: 0.7793\n", "Epoch 152/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3044 - mask_output_loss: 0.1752 - boundary_output_loss: 0.2583 - mask_output_iou_coef: 0.8194 - boundary_output_iou_coef: 0.6912\n", "Epoch 152: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 562s 2s/step - loss: 0.3044 - mask_output_loss: 0.1752 - boundary_output_loss: 0.2583 - mask_output_iou_coef: 0.8194 - boundary_output_iou_coef: 0.6912 - val_loss: 0.2096 - val_mask_output_loss: 0.1168 - val_boundary_output_loss: 0.1857 - val_mask_output_iou_coef: 0.8810 - val_boundary_output_iou_coef: 0.7790\n", "Epoch 153/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3100 - mask_output_loss: 0.1802 - boundary_output_loss: 0.2595 - mask_output_iou_coef: 0.8152 - boundary_output_iou_coef: 0.6899\n", "Epoch 153: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 572s 2s/step - loss: 0.3100 - mask_output_loss: 0.1802 - boundary_output_loss: 0.2595 - mask_output_iou_coef: 0.8152 - boundary_output_iou_coef: 0.6899 - val_loss: 0.2112 - val_mask_output_loss: 0.1182 - val_boundary_output_loss: 0.1860 - val_mask_output_iou_coef: 0.8759 - val_boundary_output_iou_coef: 0.7781\n", "Epoch 154/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3027 - mask_output_loss: 0.1737 - boundary_output_loss: 0.2579 - mask_output_iou_coef: 0.8208 - boundary_output_iou_coef: 0.6929\n", "Epoch 154: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 566s 2s/step - loss: 0.3027 - mask_output_loss: 0.1737 - boundary_output_loss: 0.2579 - mask_output_iou_coef: 0.8208 - boundary_output_iou_coef: 0.6929 - val_loss: 0.2119 - val_mask_output_loss: 0.1172 - val_boundary_output_loss: 0.1893 - val_mask_output_iou_coef: 0.8786 - val_boundary_output_iou_coef: 0.7743\n", "Epoch 155/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3157 - mask_output_loss: 0.1842 - boundary_output_loss: 0.2628 - mask_output_iou_coef: 0.8117 - boundary_output_iou_coef: 0.6873\n", "Epoch 155: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 551s 2s/step - loss: 0.3157 - mask_output_loss: 0.1842 - boundary_output_loss: 0.2628 - mask_output_iou_coef: 0.8117 - boundary_output_iou_coef: 0.6873 - val_loss: 0.2073 - val_mask_output_loss: 0.1141 - val_boundary_output_loss: 0.1864 - val_mask_output_iou_coef: 0.8797 - val_boundary_output_iou_coef: 0.7762\n", "Epoch 156/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3137 - mask_output_loss: 0.1818 - boundary_output_loss: 0.2637 - mask_output_iou_coef: 0.8134 - boundary_output_iou_coef: 0.6864\n", "Epoch 156: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 560s 2s/step - loss: 0.3137 - mask_output_loss: 0.1818 - boundary_output_loss: 0.2637 - mask_output_iou_coef: 0.8134 - boundary_output_iou_coef: 0.6864 - val_loss: 0.2074 - val_mask_output_loss: 0.1136 - val_boundary_output_loss: 0.1875 - val_mask_output_iou_coef: 0.8796 - val_boundary_output_iou_coef: 0.7766\n", "Epoch 157/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3052 - mask_output_loss: 0.1760 - boundary_output_loss: 0.2584 - mask_output_iou_coef: 0.8192 - boundary_output_iou_coef: 0.6915\n", "Epoch 157: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 575s 2s/step - loss: 0.3052 - mask_output_loss: 0.1760 - boundary_output_loss: 0.2584 - mask_output_iou_coef: 0.8192 - boundary_output_iou_coef: 0.6915 - val_loss: 0.2108 - val_mask_output_loss: 0.1157 - val_boundary_output_loss: 0.1903 - val_mask_output_iou_coef: 0.8761 - val_boundary_output_iou_coef: 0.7698\n", "Epoch 158/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3135 - mask_output_loss: 0.1839 - boundary_output_loss: 0.2593 - mask_output_iou_coef: 0.8145 - boundary_output_iou_coef: 0.6914\n", "Epoch 158: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 634s 3s/step - loss: 0.3135 - mask_output_loss: 0.1839 - boundary_output_loss: 0.2593 - mask_output_iou_coef: 0.8145 - boundary_output_iou_coef: 0.6914 - val_loss: 0.2113 - val_mask_output_loss: 0.1176 - val_boundary_output_loss: 0.1874 - val_mask_output_iou_coef: 0.8763 - val_boundary_output_iou_coef: 0.7776\n", "Epoch 159/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3102 - mask_output_loss: 0.1793 - boundary_output_loss: 0.2618 - mask_output_iou_coef: 0.8158 - boundary_output_iou_coef: 0.6885\n", "Epoch 159: val_loss did not improve from 0.20411\n", "230/230 [==============================] - 537s 2s/step - loss: 0.3102 - mask_output_loss: 0.1793 - boundary_output_loss: 0.2618 - mask_output_iou_coef: 0.8158 - boundary_output_iou_coef: 0.6885 - val_loss: 0.2110 - val_mask_output_loss: 0.1171 - val_boundary_output_loss: 0.1877 - val_mask_output_iou_coef: 0.8795 - val_boundary_output_iou_coef: 0.7774\n", "Epoch 160/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3087 - mask_output_loss: 0.1793 - boundary_output_loss: 0.2587 - mask_output_iou_coef: 0.8154 - boundary_output_iou_coef: 0.6912\n", "Epoch 160: val_loss improved from 0.20411 to 0.20288, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 538s 2s/step - loss: 0.3087 - mask_output_loss: 0.1793 - boundary_output_loss: 0.2587 - mask_output_iou_coef: 0.8154 - boundary_output_iou_coef: 0.6912 - val_loss: 0.2029 - val_mask_output_loss: 0.1121 - val_boundary_output_loss: 0.1815 - val_mask_output_iou_coef: 0.8808 - val_boundary_output_iou_coef: 0.7799\n", "Epoch 161/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3037 - mask_output_loss: 0.1732 - boundary_output_loss: 0.2609 - mask_output_iou_coef: 0.8217 - boundary_output_iou_coef: 0.6895\n", "Epoch 161: val_loss did not improve from 0.20288\n", "230/230 [==============================] - 536s 2s/step - loss: 0.3037 - mask_output_loss: 0.1732 - boundary_output_loss: 0.2609 - mask_output_iou_coef: 0.8217 - boundary_output_iou_coef: 0.6895 - val_loss: 0.2072 - val_mask_output_loss: 0.1144 - val_boundary_output_loss: 0.1856 - val_mask_output_iou_coef: 0.8767 - val_boundary_output_iou_coef: 0.7802\n", "Epoch 162/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3132 - mask_output_loss: 0.1816 - boundary_output_loss: 0.2631 - mask_output_iou_coef: 0.8134 - boundary_output_iou_coef: 0.6875\n", "Epoch 162: val_loss did not improve from 0.20288\n", "230/230 [==============================] - 538s 2s/step - loss: 0.3132 - mask_output_loss: 0.1816 - boundary_output_loss: 0.2631 - mask_output_iou_coef: 0.8134 - boundary_output_iou_coef: 0.6875 - val_loss: 0.2066 - val_mask_output_loss: 0.1157 - val_boundary_output_loss: 0.1817 - val_mask_output_iou_coef: 0.8806 - val_boundary_output_iou_coef: 0.7814\n", "Epoch 163/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3061 - mask_output_loss: 0.1780 - boundary_output_loss: 0.2562 - mask_output_iou_coef: 0.8177 - boundary_output_iou_coef: 0.6933\n", "Epoch 163: val_loss did not improve from 0.20288\n", "230/230 [==============================] - 540s 2s/step - loss: 0.3061 - mask_output_loss: 0.1780 - boundary_output_loss: 0.2562 - mask_output_iou_coef: 0.8177 - boundary_output_iou_coef: 0.6933 - val_loss: 0.2109 - val_mask_output_loss: 0.1173 - val_boundary_output_loss: 0.1871 - val_mask_output_iou_coef: 0.8805 - val_boundary_output_iou_coef: 0.7787\n", "Epoch 164/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3075 - mask_output_loss: 0.1778 - boundary_output_loss: 0.2594 - mask_output_iou_coef: 0.8173 - boundary_output_iou_coef: 0.6911\n", "Epoch 164: val_loss did not improve from 0.20288\n", "230/230 [==============================] - 536s 2s/step - loss: 0.3075 - mask_output_loss: 0.1778 - boundary_output_loss: 0.2594 - mask_output_iou_coef: 0.8173 - boundary_output_iou_coef: 0.6911 - val_loss: 0.2126 - val_mask_output_loss: 0.1192 - val_boundary_output_loss: 0.1867 - val_mask_output_iou_coef: 0.8795 - val_boundary_output_iou_coef: 0.7793\n", "Epoch 165/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3056 - mask_output_loss: 0.1774 - boundary_output_loss: 0.2564 - mask_output_iou_coef: 0.8181 - boundary_output_iou_coef: 0.6939\n", "Epoch 165: val_loss did not improve from 0.20288\n", "230/230 [==============================] - 536s 2s/step - loss: 0.3056 - mask_output_loss: 0.1774 - boundary_output_loss: 0.2564 - mask_output_iou_coef: 0.8181 - boundary_output_iou_coef: 0.6939 - val_loss: 0.2109 - val_mask_output_loss: 0.1176 - val_boundary_output_loss: 0.1866 - val_mask_output_iou_coef: 0.8770 - val_boundary_output_iou_coef: 0.7780\n", "Epoch 166/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3085 - mask_output_loss: 0.1790 - boundary_output_loss: 0.2589 - mask_output_iou_coef: 0.8164 - boundary_output_iou_coef: 0.6906\n", "Epoch 166: val_loss did not improve from 0.20288\n", "230/230 [==============================] - 534s 2s/step - loss: 0.3085 - mask_output_loss: 0.1790 - boundary_output_loss: 0.2589 - mask_output_iou_coef: 0.8164 - boundary_output_iou_coef: 0.6906 - val_loss: 0.2062 - val_mask_output_loss: 0.1154 - val_boundary_output_loss: 0.1816 - val_mask_output_iou_coef: 0.8801 - val_boundary_output_iou_coef: 0.7843\n", "Epoch 167/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3108 - mask_output_loss: 0.1801 - boundary_output_loss: 0.2615 - mask_output_iou_coef: 0.8163 - boundary_output_iou_coef: 0.6893\n", "Epoch 167: val_loss did not improve from 0.20288\n", "230/230 [==============================] - 539s 2s/step - loss: 0.3108 - mask_output_loss: 0.1801 - boundary_output_loss: 0.2615 - mask_output_iou_coef: 0.8163 - boundary_output_iou_coef: 0.6893 - val_loss: 0.2083 - val_mask_output_loss: 0.1155 - val_boundary_output_loss: 0.1857 - val_mask_output_iou_coef: 0.8790 - val_boundary_output_iou_coef: 0.7769\n", "Epoch 168/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3082 - mask_output_loss: 0.1778 - boundary_output_loss: 0.2609 - mask_output_iou_coef: 0.8177 - boundary_output_iou_coef: 0.6889\n", "Epoch 168: val_loss did not improve from 0.20288\n", "230/230 [==============================] - 539s 2s/step - loss: 0.3082 - mask_output_loss: 0.1778 - boundary_output_loss: 0.2609 - mask_output_iou_coef: 0.8177 - boundary_output_iou_coef: 0.6889 - val_loss: 0.2047 - val_mask_output_loss: 0.1128 - val_boundary_output_loss: 0.1836 - val_mask_output_iou_coef: 0.8817 - val_boundary_output_iou_coef: 0.7813\n", "Epoch 169/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2946 - mask_output_loss: 0.1672 - boundary_output_loss: 0.2550 - mask_output_iou_coef: 0.8249 - boundary_output_iou_coef: 0.6948\n", "Epoch 169: val_loss did not improve from 0.20288\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2946 - mask_output_loss: 0.1672 - boundary_output_loss: 0.2550 - mask_output_iou_coef: 0.8249 - boundary_output_iou_coef: 0.6948 - val_loss: 0.2039 - val_mask_output_loss: 0.1117 - val_boundary_output_loss: 0.1844 - val_mask_output_iou_coef: 0.8858 - val_boundary_output_iou_coef: 0.7819\n", "Epoch 170/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3060 - mask_output_loss: 0.1771 - boundary_output_loss: 0.2579 - mask_output_iou_coef: 0.8173 - boundary_output_iou_coef: 0.6921\n", "Epoch 170: val_loss improved from 0.20288 to 0.20187, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 537s 2s/step - loss: 0.3060 - mask_output_loss: 0.1771 - boundary_output_loss: 0.2579 - mask_output_iou_coef: 0.8173 - boundary_output_iou_coef: 0.6921 - val_loss: 0.2019 - val_mask_output_loss: 0.1111 - val_boundary_output_loss: 0.1816 - val_mask_output_iou_coef: 0.8836 - val_boundary_output_iou_coef: 0.7804\n", "Epoch 171/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3006 - mask_output_loss: 0.1728 - boundary_output_loss: 0.2556 - mask_output_iou_coef: 0.8229 - boundary_output_iou_coef: 0.6946\n", "Epoch 171: val_loss did not improve from 0.20187\n", "230/230 [==============================] - 539s 2s/step - loss: 0.3006 - mask_output_loss: 0.1728 - boundary_output_loss: 0.2556 - mask_output_iou_coef: 0.8229 - boundary_output_iou_coef: 0.6946 - val_loss: 0.2102 - val_mask_output_loss: 0.1160 - val_boundary_output_loss: 0.1885 - val_mask_output_iou_coef: 0.8790 - val_boundary_output_iou_coef: 0.7771\n", "Epoch 172/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3069 - mask_output_loss: 0.1786 - boundary_output_loss: 0.2568 - mask_output_iou_coef: 0.8186 - boundary_output_iou_coef: 0.6939\n", "Epoch 172: val_loss improved from 0.20187 to 0.20149, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 536s 2s/step - loss: 0.3069 - mask_output_loss: 0.1786 - boundary_output_loss: 0.2568 - mask_output_iou_coef: 0.8186 - boundary_output_iou_coef: 0.6939 - val_loss: 0.2015 - val_mask_output_loss: 0.1112 - val_boundary_output_loss: 0.1806 - val_mask_output_iou_coef: 0.8825 - val_boundary_output_iou_coef: 0.7822\n", "Epoch 173/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3119 - mask_output_loss: 0.1814 - boundary_output_loss: 0.2611 - mask_output_iou_coef: 0.8144 - boundary_output_iou_coef: 0.6897\n", "Epoch 173: val_loss improved from 0.20149 to 0.20002, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 540s 2s/step - loss: 0.3119 - mask_output_loss: 0.1814 - boundary_output_loss: 0.2611 - mask_output_iou_coef: 0.8144 - boundary_output_iou_coef: 0.6897 - val_loss: 0.2000 - val_mask_output_loss: 0.1106 - val_boundary_output_loss: 0.1788 - val_mask_output_iou_coef: 0.8836 - val_boundary_output_iou_coef: 0.7855\n", "Epoch 174/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3053 - mask_output_loss: 0.1747 - boundary_output_loss: 0.2611 - mask_output_iou_coef: 0.8180 - boundary_output_iou_coef: 0.6888\n", "Epoch 174: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 541s 2s/step - loss: 0.3053 - mask_output_loss: 0.1747 - boundary_output_loss: 0.2611 - mask_output_iou_coef: 0.8180 - boundary_output_iou_coef: 0.6888 - val_loss: 0.2073 - val_mask_output_loss: 0.1144 - val_boundary_output_loss: 0.1856 - val_mask_output_iou_coef: 0.8799 - val_boundary_output_iou_coef: 0.7775\n", "Epoch 175/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3056 - mask_output_loss: 0.1784 - boundary_output_loss: 0.2544 - mask_output_iou_coef: 0.8179 - boundary_output_iou_coef: 0.6954\n", "Epoch 175: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 544s 2s/step - loss: 0.3056 - mask_output_loss: 0.1784 - boundary_output_loss: 0.2544 - mask_output_iou_coef: 0.8179 - boundary_output_iou_coef: 0.6954 - val_loss: 0.2077 - val_mask_output_loss: 0.1158 - val_boundary_output_loss: 0.1838 - val_mask_output_iou_coef: 0.8789 - val_boundary_output_iou_coef: 0.7796\n", "Epoch 176/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3047 - mask_output_loss: 0.1752 - boundary_output_loss: 0.2591 - mask_output_iou_coef: 0.8181 - boundary_output_iou_coef: 0.6917\n", "Epoch 176: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 541s 2s/step - loss: 0.3047 - mask_output_loss: 0.1752 - boundary_output_loss: 0.2591 - mask_output_iou_coef: 0.8181 - boundary_output_iou_coef: 0.6917 - val_loss: 0.2079 - val_mask_output_loss: 0.1154 - val_boundary_output_loss: 0.1850 - val_mask_output_iou_coef: 0.8804 - val_boundary_output_iou_coef: 0.7765\n", "Epoch 177/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3079 - mask_output_loss: 0.1779 - boundary_output_loss: 0.2600 - mask_output_iou_coef: 0.8191 - boundary_output_iou_coef: 0.6910\n", "Epoch 177: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 545s 2s/step - loss: 0.3079 - mask_output_loss: 0.1779 - boundary_output_loss: 0.2600 - mask_output_iou_coef: 0.8191 - boundary_output_iou_coef: 0.6910 - val_loss: 0.2019 - val_mask_output_loss: 0.1120 - val_boundary_output_loss: 0.1798 - val_mask_output_iou_coef: 0.8814 - val_boundary_output_iou_coef: 0.7813\n", "Epoch 178/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3090 - mask_output_loss: 0.1806 - boundary_output_loss: 0.2569 - mask_output_iou_coef: 0.8158 - boundary_output_iou_coef: 0.6928\n", "Epoch 178: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 531s 2s/step - loss: 0.3090 - mask_output_loss: 0.1806 - boundary_output_loss: 0.2569 - mask_output_iou_coef: 0.8158 - boundary_output_iou_coef: 0.6928 - val_loss: 0.2091 - val_mask_output_loss: 0.1165 - val_boundary_output_loss: 0.1853 - val_mask_output_iou_coef: 0.8769 - val_boundary_output_iou_coef: 0.7760\n", "Epoch 179/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3065 - mask_output_loss: 0.1784 - boundary_output_loss: 0.2562 - mask_output_iou_coef: 0.8181 - boundary_output_iou_coef: 0.6941\n", "Epoch 179: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 539s 2s/step - loss: 0.3065 - mask_output_loss: 0.1784 - boundary_output_loss: 0.2562 - mask_output_iou_coef: 0.8181 - boundary_output_iou_coef: 0.6941 - val_loss: 0.2054 - val_mask_output_loss: 0.1136 - val_boundary_output_loss: 0.1835 - val_mask_output_iou_coef: 0.8800 - val_boundary_output_iou_coef: 0.7791\n", "Epoch 180/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3018 - mask_output_loss: 0.1748 - boundary_output_loss: 0.2540 - mask_output_iou_coef: 0.8210 - boundary_output_iou_coef: 0.6969\n", "Epoch 180: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 541s 2s/step - loss: 0.3018 - mask_output_loss: 0.1748 - boundary_output_loss: 0.2540 - mask_output_iou_coef: 0.8210 - boundary_output_iou_coef: 0.6969 - val_loss: 0.2047 - val_mask_output_loss: 0.1134 - val_boundary_output_loss: 0.1825 - val_mask_output_iou_coef: 0.8817 - val_boundary_output_iou_coef: 0.7819\n", "Epoch 181/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3109 - mask_output_loss: 0.1822 - boundary_output_loss: 0.2574 - mask_output_iou_coef: 0.8160 - boundary_output_iou_coef: 0.6934\n", "Epoch 181: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 539s 2s/step - loss: 0.3109 - mask_output_loss: 0.1822 - boundary_output_loss: 0.2574 - mask_output_iou_coef: 0.8160 - boundary_output_iou_coef: 0.6934 - val_loss: 0.2060 - val_mask_output_loss: 0.1135 - val_boundary_output_loss: 0.1850 - val_mask_output_iou_coef: 0.8783 - val_boundary_output_iou_coef: 0.7772\n", "Epoch 182/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3058 - mask_output_loss: 0.1757 - boundary_output_loss: 0.2603 - mask_output_iou_coef: 0.8196 - boundary_output_iou_coef: 0.6905\n", "Epoch 182: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 538s 2s/step - loss: 0.3058 - mask_output_loss: 0.1757 - boundary_output_loss: 0.2603 - mask_output_iou_coef: 0.8196 - boundary_output_iou_coef: 0.6905 - val_loss: 0.2082 - val_mask_output_loss: 0.1138 - val_boundary_output_loss: 0.1888 - val_mask_output_iou_coef: 0.8801 - val_boundary_output_iou_coef: 0.7760\n", "Epoch 183/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3030 - mask_output_loss: 0.1753 - boundary_output_loss: 0.2554 - mask_output_iou_coef: 0.8202 - boundary_output_iou_coef: 0.6950\n", "Epoch 183: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 543s 2s/step - loss: 0.3030 - mask_output_loss: 0.1753 - boundary_output_loss: 0.2554 - mask_output_iou_coef: 0.8202 - boundary_output_iou_coef: 0.6950 - val_loss: 0.2021 - val_mask_output_loss: 0.1104 - val_boundary_output_loss: 0.1835 - val_mask_output_iou_coef: 0.8850 - val_boundary_output_iou_coef: 0.7804\n", "Epoch 184/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2990 - mask_output_loss: 0.1709 - boundary_output_loss: 0.2561 - mask_output_iou_coef: 0.8224 - boundary_output_iou_coef: 0.6937\n", "Epoch 184: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2990 - mask_output_loss: 0.1709 - boundary_output_loss: 0.2561 - mask_output_iou_coef: 0.8224 - boundary_output_iou_coef: 0.6937 - val_loss: 0.2077 - val_mask_output_loss: 0.1154 - val_boundary_output_loss: 0.1847 - val_mask_output_iou_coef: 0.8803 - val_boundary_output_iou_coef: 0.7792\n", "Epoch 185/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3076 - mask_output_loss: 0.1776 - boundary_output_loss: 0.2601 - mask_output_iou_coef: 0.8182 - boundary_output_iou_coef: 0.6907\n", "Epoch 185: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 559s 2s/step - loss: 0.3076 - mask_output_loss: 0.1776 - boundary_output_loss: 0.2601 - mask_output_iou_coef: 0.8182 - boundary_output_iou_coef: 0.6907 - val_loss: 0.2046 - val_mask_output_loss: 0.1127 - val_boundary_output_loss: 0.1838 - val_mask_output_iou_coef: 0.8829 - val_boundary_output_iou_coef: 0.7794\n", "Epoch 186/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2970 - mask_output_loss: 0.1696 - boundary_output_loss: 0.2548 - mask_output_iou_coef: 0.8250 - boundary_output_iou_coef: 0.6952\n", "Epoch 186: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 561s 2s/step - loss: 0.2970 - mask_output_loss: 0.1696 - boundary_output_loss: 0.2548 - mask_output_iou_coef: 0.8250 - boundary_output_iou_coef: 0.6952 - val_loss: 0.2019 - val_mask_output_loss: 0.1109 - val_boundary_output_loss: 0.1820 - val_mask_output_iou_coef: 0.8836 - val_boundary_output_iou_coef: 0.7834\n", "Epoch 187/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2960 - mask_output_loss: 0.1714 - boundary_output_loss: 0.2493 - mask_output_iou_coef: 0.8234 - boundary_output_iou_coef: 0.7014\n", "Epoch 187: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 562s 2s/step - loss: 0.2960 - mask_output_loss: 0.1714 - boundary_output_loss: 0.2493 - mask_output_iou_coef: 0.8234 - boundary_output_iou_coef: 0.7014 - val_loss: 0.2080 - val_mask_output_loss: 0.1156 - val_boundary_output_loss: 0.1849 - val_mask_output_iou_coef: 0.8801 - val_boundary_output_iou_coef: 0.7791\n", "Epoch 188/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3031 - mask_output_loss: 0.1736 - boundary_output_loss: 0.2589 - mask_output_iou_coef: 0.8217 - boundary_output_iou_coef: 0.6914\n", "Epoch 188: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 564s 2s/step - loss: 0.3031 - mask_output_loss: 0.1736 - boundary_output_loss: 0.2589 - mask_output_iou_coef: 0.8217 - boundary_output_iou_coef: 0.6914 - val_loss: 0.2051 - val_mask_output_loss: 0.1134 - val_boundary_output_loss: 0.1834 - val_mask_output_iou_coef: 0.8847 - val_boundary_output_iou_coef: 0.7822\n", "Epoch 189/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2977 - mask_output_loss: 0.1706 - boundary_output_loss: 0.2543 - mask_output_iou_coef: 0.8247 - boundary_output_iou_coef: 0.6965\n", "Epoch 189: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 559s 2s/step - loss: 0.2977 - mask_output_loss: 0.1706 - boundary_output_loss: 0.2543 - mask_output_iou_coef: 0.8247 - boundary_output_iou_coef: 0.6965 - val_loss: 0.2094 - val_mask_output_loss: 0.1155 - val_boundary_output_loss: 0.1878 - val_mask_output_iou_coef: 0.8816 - val_boundary_output_iou_coef: 0.7777\n", "Epoch 190/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2962 - mask_output_loss: 0.1683 - boundary_output_loss: 0.2559 - mask_output_iou_coef: 0.8279 - boundary_output_iou_coef: 0.6957\n", "Epoch 190: val_loss did not improve from 0.20002\n", "230/230 [==============================] - 560s 2s/step - loss: 0.2962 - mask_output_loss: 0.1683 - boundary_output_loss: 0.2559 - mask_output_iou_coef: 0.8279 - boundary_output_iou_coef: 0.6957 - val_loss: 0.2033 - val_mask_output_loss: 0.1124 - val_boundary_output_loss: 0.1818 - val_mask_output_iou_coef: 0.8822 - val_boundary_output_iou_coef: 0.7843\n", "Epoch 191/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3012 - mask_output_loss: 0.1724 - boundary_output_loss: 0.2576 - mask_output_iou_coef: 0.8214 - boundary_output_iou_coef: 0.6925\n", "Epoch 191: val_loss improved from 0.20002 to 0.19744, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 561s 2s/step - loss: 0.3012 - mask_output_loss: 0.1724 - boundary_output_loss: 0.2576 - mask_output_iou_coef: 0.8214 - boundary_output_iou_coef: 0.6925 - val_loss: 0.1974 - val_mask_output_loss: 0.1080 - val_boundary_output_loss: 0.1789 - val_mask_output_iou_coef: 0.8871 - val_boundary_output_iou_coef: 0.7835\n", "Epoch 192/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2967 - mask_output_loss: 0.1687 - boundary_output_loss: 0.2561 - mask_output_iou_coef: 0.8233 - boundary_output_iou_coef: 0.6940\n", "Epoch 192: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 560s 2s/step - loss: 0.2967 - mask_output_loss: 0.1687 - boundary_output_loss: 0.2561 - mask_output_iou_coef: 0.8233 - boundary_output_iou_coef: 0.6940 - val_loss: 0.2014 - val_mask_output_loss: 0.1106 - val_boundary_output_loss: 0.1815 - val_mask_output_iou_coef: 0.8844 - val_boundary_output_iou_coef: 0.7815\n", "Epoch 193/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2970 - mask_output_loss: 0.1711 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8239 - boundary_output_iou_coef: 0.6980\n", "Epoch 193: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 558s 2s/step - loss: 0.2970 - mask_output_loss: 0.1711 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8239 - boundary_output_iou_coef: 0.6980 - val_loss: 0.2041 - val_mask_output_loss: 0.1118 - val_boundary_output_loss: 0.1845 - val_mask_output_iou_coef: 0.8829 - val_boundary_output_iou_coef: 0.7801\n", "Epoch 194/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3067 - mask_output_loss: 0.1736 - boundary_output_loss: 0.2661 - mask_output_iou_coef: 0.8209 - boundary_output_iou_coef: 0.6841\n", "Epoch 194: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 561s 2s/step - loss: 0.3067 - mask_output_loss: 0.1736 - boundary_output_loss: 0.2661 - mask_output_iou_coef: 0.8209 - boundary_output_iou_coef: 0.6841 - val_loss: 0.2075 - val_mask_output_loss: 0.1133 - val_boundary_output_loss: 0.1884 - val_mask_output_iou_coef: 0.8831 - val_boundary_output_iou_coef: 0.7770\n", "Epoch 195/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3214 - mask_output_loss: 0.1854 - boundary_output_loss: 0.2720 - mask_output_iou_coef: 0.8112 - boundary_output_iou_coef: 0.6780\n", "Epoch 195: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 565s 2s/step - loss: 0.3214 - mask_output_loss: 0.1854 - boundary_output_loss: 0.2720 - mask_output_iou_coef: 0.8112 - boundary_output_iou_coef: 0.6780 - val_loss: 0.2050 - val_mask_output_loss: 0.1129 - val_boundary_output_loss: 0.1842 - val_mask_output_iou_coef: 0.8833 - val_boundary_output_iou_coef: 0.7811\n", "Epoch 196/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3114 - mask_output_loss: 0.1806 - boundary_output_loss: 0.2617 - mask_output_iou_coef: 0.8152 - boundary_output_iou_coef: 0.6881\n", "Epoch 196: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 565s 2s/step - loss: 0.3114 - mask_output_loss: 0.1806 - boundary_output_loss: 0.2617 - mask_output_iou_coef: 0.8152 - boundary_output_iou_coef: 0.6881 - val_loss: 0.1996 - val_mask_output_loss: 0.1090 - val_boundary_output_loss: 0.1812 - val_mask_output_iou_coef: 0.8872 - val_boundary_output_iou_coef: 0.7831\n", "Epoch 197/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3036 - mask_output_loss: 0.1739 - boundary_output_loss: 0.2594 - mask_output_iou_coef: 0.8213 - boundary_output_iou_coef: 0.6904\n", "Epoch 197: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 563s 2s/step - loss: 0.3036 - mask_output_loss: 0.1739 - boundary_output_loss: 0.2594 - mask_output_iou_coef: 0.8213 - boundary_output_iou_coef: 0.6904 - val_loss: 0.2032 - val_mask_output_loss: 0.1099 - val_boundary_output_loss: 0.1865 - val_mask_output_iou_coef: 0.8859 - val_boundary_output_iou_coef: 0.7812\n", "Epoch 198/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3020 - mask_output_loss: 0.1738 - boundary_output_loss: 0.2565 - mask_output_iou_coef: 0.8209 - boundary_output_iou_coef: 0.6937\n", "Epoch 198: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 569s 2s/step - loss: 0.3020 - mask_output_loss: 0.1738 - boundary_output_loss: 0.2565 - mask_output_iou_coef: 0.8209 - boundary_output_iou_coef: 0.6937 - val_loss: 0.2013 - val_mask_output_loss: 0.1108 - val_boundary_output_loss: 0.1811 - val_mask_output_iou_coef: 0.8839 - val_boundary_output_iou_coef: 0.7828\n", "Epoch 199/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3024 - mask_output_loss: 0.1739 - boundary_output_loss: 0.2571 - mask_output_iou_coef: 0.8202 - boundary_output_iou_coef: 0.6920\n", "Epoch 199: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 570s 2s/step - loss: 0.3024 - mask_output_loss: 0.1739 - boundary_output_loss: 0.2571 - mask_output_iou_coef: 0.8202 - boundary_output_iou_coef: 0.6920 - val_loss: 0.2023 - val_mask_output_loss: 0.1111 - val_boundary_output_loss: 0.1823 - val_mask_output_iou_coef: 0.8862 - val_boundary_output_iou_coef: 0.7848\n", "Epoch 200/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3046 - mask_output_loss: 0.1770 - boundary_output_loss: 0.2554 - mask_output_iou_coef: 0.8187 - boundary_output_iou_coef: 0.6948\n", "Epoch 200: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 546s 2s/step - loss: 0.3046 - mask_output_loss: 0.1770 - boundary_output_loss: 0.2554 - mask_output_iou_coef: 0.8187 - boundary_output_iou_coef: 0.6948 - val_loss: 0.2030 - val_mask_output_loss: 0.1136 - val_boundary_output_loss: 0.1788 - val_mask_output_iou_coef: 0.8826 - val_boundary_output_iou_coef: 0.7861\n", "Epoch 201/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3066 - mask_output_loss: 0.1777 - boundary_output_loss: 0.2579 - mask_output_iou_coef: 0.8192 - boundary_output_iou_coef: 0.6927\n", "Epoch 201: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 545s 2s/step - loss: 0.3066 - mask_output_loss: 0.1777 - boundary_output_loss: 0.2579 - mask_output_iou_coef: 0.8192 - boundary_output_iou_coef: 0.6927 - val_loss: 0.2067 - val_mask_output_loss: 0.1144 - val_boundary_output_loss: 0.1845 - val_mask_output_iou_coef: 0.8813 - val_boundary_output_iou_coef: 0.7814\n", "Epoch 202/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2994 - mask_output_loss: 0.1715 - boundary_output_loss: 0.2557 - mask_output_iou_coef: 0.8226 - boundary_output_iou_coef: 0.6945\n", "Epoch 202: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2994 - mask_output_loss: 0.1715 - boundary_output_loss: 0.2557 - mask_output_iou_coef: 0.8226 - boundary_output_iou_coef: 0.6945 - val_loss: 0.2033 - val_mask_output_loss: 0.1117 - val_boundary_output_loss: 0.1832 - val_mask_output_iou_coef: 0.8837 - val_boundary_output_iou_coef: 0.7824\n", "Epoch 203/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2987 - mask_output_loss: 0.1704 - boundary_output_loss: 0.2565 - mask_output_iou_coef: 0.8255 - boundary_output_iou_coef: 0.6943\n", "Epoch 203: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2987 - mask_output_loss: 0.1704 - boundary_output_loss: 0.2565 - mask_output_iou_coef: 0.8255 - boundary_output_iou_coef: 0.6943 - val_loss: 0.2023 - val_mask_output_loss: 0.1110 - val_boundary_output_loss: 0.1827 - val_mask_output_iou_coef: 0.8859 - val_boundary_output_iou_coef: 0.7844\n", "Epoch 204/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2943 - mask_output_loss: 0.1681 - boundary_output_loss: 0.2523 - mask_output_iou_coef: 0.8268 - boundary_output_iou_coef: 0.6980\n", "Epoch 204: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2943 - mask_output_loss: 0.1681 - boundary_output_loss: 0.2523 - mask_output_iou_coef: 0.8268 - boundary_output_iou_coef: 0.6980 - val_loss: 0.1986 - val_mask_output_loss: 0.1092 - val_boundary_output_loss: 0.1788 - val_mask_output_iou_coef: 0.8853 - val_boundary_output_iou_coef: 0.7827\n", "Epoch 205/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2946 - mask_output_loss: 0.1671 - boundary_output_loss: 0.2549 - mask_output_iou_coef: 0.8269 - boundary_output_iou_coef: 0.6959\n", "Epoch 205: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 549s 2s/step - loss: 0.2946 - mask_output_loss: 0.1671 - boundary_output_loss: 0.2549 - mask_output_iou_coef: 0.8269 - boundary_output_iou_coef: 0.6959 - val_loss: 0.2028 - val_mask_output_loss: 0.1119 - val_boundary_output_loss: 0.1818 - val_mask_output_iou_coef: 0.8873 - val_boundary_output_iou_coef: 0.7837\n", "Epoch 206/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2984 - mask_output_loss: 0.1710 - boundary_output_loss: 0.2548 - mask_output_iou_coef: 0.8236 - boundary_output_iou_coef: 0.6952\n", "Epoch 206: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 550s 2s/step - loss: 0.2984 - mask_output_loss: 0.1710 - boundary_output_loss: 0.2548 - mask_output_iou_coef: 0.8236 - boundary_output_iou_coef: 0.6952 - val_loss: 0.1982 - val_mask_output_loss: 0.1085 - val_boundary_output_loss: 0.1793 - val_mask_output_iou_coef: 0.8882 - val_boundary_output_iou_coef: 0.7862\n", "Epoch 207/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3139 - mask_output_loss: 0.1845 - boundary_output_loss: 0.2588 - mask_output_iou_coef: 0.8130 - boundary_output_iou_coef: 0.6915\n", "Epoch 207: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 551s 2s/step - loss: 0.3139 - mask_output_loss: 0.1845 - boundary_output_loss: 0.2588 - mask_output_iou_coef: 0.8130 - boundary_output_iou_coef: 0.6915 - val_loss: 0.2063 - val_mask_output_loss: 0.1144 - val_boundary_output_loss: 0.1837 - val_mask_output_iou_coef: 0.8816 - val_boundary_output_iou_coef: 0.7810\n", "Epoch 208/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2973 - mask_output_loss: 0.1725 - boundary_output_loss: 0.2496 - mask_output_iou_coef: 0.8212 - boundary_output_iou_coef: 0.7008\n", "Epoch 208: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2973 - mask_output_loss: 0.1725 - boundary_output_loss: 0.2496 - mask_output_iou_coef: 0.8212 - boundary_output_iou_coef: 0.7008 - val_loss: 0.2011 - val_mask_output_loss: 0.1112 - val_boundary_output_loss: 0.1797 - val_mask_output_iou_coef: 0.8855 - val_boundary_output_iou_coef: 0.7860\n", "Epoch 209/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3035 - mask_output_loss: 0.1760 - boundary_output_loss: 0.2549 - mask_output_iou_coef: 0.8223 - boundary_output_iou_coef: 0.6957\n", "Epoch 209: val_loss did not improve from 0.19744\n", "230/230 [==============================] - 552s 2s/step - loss: 0.3035 - mask_output_loss: 0.1760 - boundary_output_loss: 0.2549 - mask_output_iou_coef: 0.8223 - boundary_output_iou_coef: 0.6957 - val_loss: 0.2037 - val_mask_output_loss: 0.1123 - val_boundary_output_loss: 0.1827 - val_mask_output_iou_coef: 0.8834 - val_boundary_output_iou_coef: 0.7841\n", "Epoch 210/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2976 - mask_output_loss: 0.1720 - boundary_output_loss: 0.2512 - mask_output_iou_coef: 0.8211 - boundary_output_iou_coef: 0.6984\n", "Epoch 210: val_loss improved from 0.19744 to 0.19711, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2976 - mask_output_loss: 0.1720 - boundary_output_loss: 0.2512 - mask_output_iou_coef: 0.8211 - boundary_output_iou_coef: 0.6984 - val_loss: 0.1971 - val_mask_output_loss: 0.1080 - val_boundary_output_loss: 0.1783 - val_mask_output_iou_coef: 0.8852 - val_boundary_output_iou_coef: 0.7851\n", "Epoch 211/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2898 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2554 - mask_output_iou_coef: 0.8308 - boundary_output_iou_coef: 0.6948\n", "Epoch 211: val_loss did not improve from 0.19711\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2898 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2554 - mask_output_iou_coef: 0.8308 - boundary_output_iou_coef: 0.6948 - val_loss: 0.2065 - val_mask_output_loss: 0.1145 - val_boundary_output_loss: 0.1841 - val_mask_output_iou_coef: 0.8861 - val_boundary_output_iou_coef: 0.7838\n", "Epoch 212/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2884 - mask_output_loss: 0.1623 - boundary_output_loss: 0.2522 - mask_output_iou_coef: 0.8308 - boundary_output_iou_coef: 0.6977\n", "Epoch 212: val_loss did not improve from 0.19711\n", "230/230 [==============================] - 563s 2s/step - loss: 0.2884 - mask_output_loss: 0.1623 - boundary_output_loss: 0.2522 - mask_output_iou_coef: 0.8308 - boundary_output_iou_coef: 0.6977 - val_loss: 0.2000 - val_mask_output_loss: 0.1087 - val_boundary_output_loss: 0.1825 - val_mask_output_iou_coef: 0.8855 - val_boundary_output_iou_coef: 0.7789\n", "Epoch 213/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2994 - mask_output_loss: 0.1727 - boundary_output_loss: 0.2535 - mask_output_iou_coef: 0.8233 - boundary_output_iou_coef: 0.6969\n", "Epoch 213: val_loss improved from 0.19711 to 0.19503, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 563s 2s/step - loss: 0.2994 - mask_output_loss: 0.1727 - boundary_output_loss: 0.2535 - mask_output_iou_coef: 0.8233 - boundary_output_iou_coef: 0.6969 - val_loss: 0.1950 - val_mask_output_loss: 0.1058 - val_boundary_output_loss: 0.1785 - val_mask_output_iou_coef: 0.8880 - val_boundary_output_iou_coef: 0.7858\n", "Epoch 214/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2993 - mask_output_loss: 0.1727 - boundary_output_loss: 0.2533 - mask_output_iou_coef: 0.8215 - boundary_output_iou_coef: 0.6975\n", "Epoch 214: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 571s 2s/step - loss: 0.2993 - mask_output_loss: 0.1727 - boundary_output_loss: 0.2533 - mask_output_iou_coef: 0.8215 - boundary_output_iou_coef: 0.6975 - val_loss: 0.2015 - val_mask_output_loss: 0.1111 - val_boundary_output_loss: 0.1807 - val_mask_output_iou_coef: 0.8839 - val_boundary_output_iou_coef: 0.7814\n", "Epoch 215/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3052 - mask_output_loss: 0.1779 - boundary_output_loss: 0.2547 - mask_output_iou_coef: 0.8194 - boundary_output_iou_coef: 0.6952\n", "Epoch 215: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 574s 2s/step - loss: 0.3052 - mask_output_loss: 0.1779 - boundary_output_loss: 0.2547 - mask_output_iou_coef: 0.8194 - boundary_output_iou_coef: 0.6952 - val_loss: 0.2014 - val_mask_output_loss: 0.1109 - val_boundary_output_loss: 0.1810 - val_mask_output_iou_coef: 0.8801 - val_boundary_output_iou_coef: 0.7834\n", "Epoch 216/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2972 - mask_output_loss: 0.1721 - boundary_output_loss: 0.2501 - mask_output_iou_coef: 0.8235 - boundary_output_iou_coef: 0.7006\n", "Epoch 216: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 571s 2s/step - loss: 0.2972 - mask_output_loss: 0.1721 - boundary_output_loss: 0.2501 - mask_output_iou_coef: 0.8235 - boundary_output_iou_coef: 0.7006 - val_loss: 0.2047 - val_mask_output_loss: 0.1133 - val_boundary_output_loss: 0.1826 - val_mask_output_iou_coef: 0.8826 - val_boundary_output_iou_coef: 0.7821\n", "Epoch 217/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3031 - mask_output_loss: 0.1765 - boundary_output_loss: 0.2533 - mask_output_iou_coef: 0.8195 - boundary_output_iou_coef: 0.6970\n", "Epoch 217: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 575s 2s/step - loss: 0.3031 - mask_output_loss: 0.1765 - boundary_output_loss: 0.2533 - mask_output_iou_coef: 0.8195 - boundary_output_iou_coef: 0.6970 - val_loss: 0.2041 - val_mask_output_loss: 0.1129 - val_boundary_output_loss: 0.1825 - val_mask_output_iou_coef: 0.8812 - val_boundary_output_iou_coef: 0.7804\n", "Epoch 218/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2927 - mask_output_loss: 0.1667 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8286 - boundary_output_iou_coef: 0.6987\n", "Epoch 218: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 582s 3s/step - loss: 0.2927 - mask_output_loss: 0.1667 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8286 - boundary_output_iou_coef: 0.6987 - val_loss: 0.1984 - val_mask_output_loss: 0.1100 - val_boundary_output_loss: 0.1768 - val_mask_output_iou_coef: 0.8854 - val_boundary_output_iou_coef: 0.7863\n", "Epoch 219/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3025 - mask_output_loss: 0.1749 - boundary_output_loss: 0.2553 - mask_output_iou_coef: 0.8217 - boundary_output_iou_coef: 0.6950\n", "Epoch 219: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 577s 3s/step - loss: 0.3025 - mask_output_loss: 0.1749 - boundary_output_loss: 0.2553 - mask_output_iou_coef: 0.8217 - boundary_output_iou_coef: 0.6950 - val_loss: 0.2089 - val_mask_output_loss: 0.1158 - val_boundary_output_loss: 0.1861 - val_mask_output_iou_coef: 0.8771 - val_boundary_output_iou_coef: 0.7801\n", "Epoch 220/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2977 - mask_output_loss: 0.1704 - boundary_output_loss: 0.2546 - mask_output_iou_coef: 0.8224 - boundary_output_iou_coef: 0.6953\n", "Epoch 220: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2977 - mask_output_loss: 0.1704 - boundary_output_loss: 0.2546 - mask_output_iou_coef: 0.8224 - boundary_output_iou_coef: 0.6953 - val_loss: 0.1974 - val_mask_output_loss: 0.1088 - val_boundary_output_loss: 0.1773 - val_mask_output_iou_coef: 0.8866 - val_boundary_output_iou_coef: 0.7870\n", "Epoch 221/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2992 - mask_output_loss: 0.1736 - boundary_output_loss: 0.2512 - mask_output_iou_coef: 0.8222 - boundary_output_iou_coef: 0.6996\n", "Epoch 221: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2992 - mask_output_loss: 0.1736 - boundary_output_loss: 0.2512 - mask_output_iou_coef: 0.8222 - boundary_output_iou_coef: 0.6996 - val_loss: 0.1955 - val_mask_output_loss: 0.1071 - val_boundary_output_loss: 0.1769 - val_mask_output_iou_coef: 0.8851 - val_boundary_output_iou_coef: 0.7832\n", "Epoch 222/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2902 - mask_output_loss: 0.1644 - boundary_output_loss: 0.2517 - mask_output_iou_coef: 0.8300 - boundary_output_iou_coef: 0.6993\n", "Epoch 222: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 543s 2s/step - loss: 0.2902 - mask_output_loss: 0.1644 - boundary_output_loss: 0.2517 - mask_output_iou_coef: 0.8300 - boundary_output_iou_coef: 0.6993 - val_loss: 0.1986 - val_mask_output_loss: 0.1089 - val_boundary_output_loss: 0.1794 - val_mask_output_iou_coef: 0.8857 - val_boundary_output_iou_coef: 0.7852\n", "Epoch 223/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2940 - mask_output_loss: 0.1701 - boundary_output_loss: 0.2479 - mask_output_iou_coef: 0.8249 - boundary_output_iou_coef: 0.7026\n", "Epoch 223: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2940 - mask_output_loss: 0.1701 - boundary_output_loss: 0.2479 - mask_output_iou_coef: 0.8249 - boundary_output_iou_coef: 0.7026 - val_loss: 0.1986 - val_mask_output_loss: 0.1094 - val_boundary_output_loss: 0.1785 - val_mask_output_iou_coef: 0.8869 - val_boundary_output_iou_coef: 0.7862\n", "Epoch 224/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2971 - mask_output_loss: 0.1703 - boundary_output_loss: 0.2536 - mask_output_iou_coef: 0.8229 - boundary_output_iou_coef: 0.6965\n", "Epoch 224: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2971 - mask_output_loss: 0.1703 - boundary_output_loss: 0.2536 - mask_output_iou_coef: 0.8229 - boundary_output_iou_coef: 0.6965 - val_loss: 0.2005 - val_mask_output_loss: 0.1093 - val_boundary_output_loss: 0.1824 - val_mask_output_iou_coef: 0.8877 - val_boundary_output_iou_coef: 0.7801\n", "Epoch 225/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2998 - mask_output_loss: 0.1735 - boundary_output_loss: 0.2525 - mask_output_iou_coef: 0.8235 - boundary_output_iou_coef: 0.6978\n", "Epoch 225: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2998 - mask_output_loss: 0.1735 - boundary_output_loss: 0.2525 - mask_output_iou_coef: 0.8235 - boundary_output_iou_coef: 0.6978 - val_loss: 0.2001 - val_mask_output_loss: 0.1105 - val_boundary_output_loss: 0.1792 - val_mask_output_iou_coef: 0.8856 - val_boundary_output_iou_coef: 0.7842\n", "Epoch 226/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2921 - mask_output_loss: 0.1670 - boundary_output_loss: 0.2503 - mask_output_iou_coef: 0.8273 - boundary_output_iou_coef: 0.7009\n", "Epoch 226: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 547s 2s/step - loss: 0.2921 - mask_output_loss: 0.1670 - boundary_output_loss: 0.2503 - mask_output_iou_coef: 0.8273 - boundary_output_iou_coef: 0.7009 - val_loss: 0.1974 - val_mask_output_loss: 0.1092 - val_boundary_output_loss: 0.1764 - val_mask_output_iou_coef: 0.8850 - val_boundary_output_iou_coef: 0.7866\n", "Epoch 227/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2947 - mask_output_loss: 0.1682 - boundary_output_loss: 0.2528 - mask_output_iou_coef: 0.8256 - boundary_output_iou_coef: 0.6980\n", "Epoch 227: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2947 - mask_output_loss: 0.1682 - boundary_output_loss: 0.2528 - mask_output_iou_coef: 0.8256 - boundary_output_iou_coef: 0.6980 - val_loss: 0.1954 - val_mask_output_loss: 0.1063 - val_boundary_output_loss: 0.1783 - val_mask_output_iou_coef: 0.8891 - val_boundary_output_iou_coef: 0.7885\n", "Epoch 228/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3056 - mask_output_loss: 0.1766 - boundary_output_loss: 0.2581 - mask_output_iou_coef: 0.8205 - boundary_output_iou_coef: 0.6923\n", "Epoch 228: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 550s 2s/step - loss: 0.3056 - mask_output_loss: 0.1766 - boundary_output_loss: 0.2581 - mask_output_iou_coef: 0.8205 - boundary_output_iou_coef: 0.6923 - val_loss: 0.2064 - val_mask_output_loss: 0.1132 - val_boundary_output_loss: 0.1863 - val_mask_output_iou_coef: 0.8802 - val_boundary_output_iou_coef: 0.7780\n", "Epoch 229/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2958 - mask_output_loss: 0.1697 - boundary_output_loss: 0.2522 - mask_output_iou_coef: 0.8245 - boundary_output_iou_coef: 0.6976\n", "Epoch 229: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2958 - mask_output_loss: 0.1697 - boundary_output_loss: 0.2522 - mask_output_iou_coef: 0.8245 - boundary_output_iou_coef: 0.6976 - val_loss: 0.1969 - val_mask_output_loss: 0.1076 - val_boundary_output_loss: 0.1785 - val_mask_output_iou_coef: 0.8847 - val_boundary_output_iou_coef: 0.7832\n", "Epoch 230/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2953 - mask_output_loss: 0.1709 - boundary_output_loss: 0.2489 - mask_output_iou_coef: 0.8251 - boundary_output_iou_coef: 0.7028\n", "Epoch 230: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2953 - mask_output_loss: 0.1709 - boundary_output_loss: 0.2489 - mask_output_iou_coef: 0.8251 - boundary_output_iou_coef: 0.7028 - val_loss: 0.2039 - val_mask_output_loss: 0.1130 - val_boundary_output_loss: 0.1817 - val_mask_output_iou_coef: 0.8833 - val_boundary_output_iou_coef: 0.7836\n", "Epoch 231/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3015 - mask_output_loss: 0.1740 - boundary_output_loss: 0.2549 - mask_output_iou_coef: 0.8205 - boundary_output_iou_coef: 0.6959\n", "Epoch 231: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 549s 2s/step - loss: 0.3015 - mask_output_loss: 0.1740 - boundary_output_loss: 0.2549 - mask_output_iou_coef: 0.8205 - boundary_output_iou_coef: 0.6959 - val_loss: 0.1990 - val_mask_output_loss: 0.1103 - val_boundary_output_loss: 0.1775 - val_mask_output_iou_coef: 0.8858 - val_boundary_output_iou_coef: 0.7880\n", "Epoch 232/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3041 - mask_output_loss: 0.1776 - boundary_output_loss: 0.2531 - mask_output_iou_coef: 0.8203 - boundary_output_iou_coef: 0.6969\n", "Epoch 232: val_loss did not improve from 0.19503\n", "230/230 [==============================] - 546s 2s/step - loss: 0.3041 - mask_output_loss: 0.1776 - boundary_output_loss: 0.2531 - mask_output_iou_coef: 0.8203 - boundary_output_iou_coef: 0.6969 - val_loss: 0.2025 - val_mask_output_loss: 0.1116 - val_boundary_output_loss: 0.1818 - val_mask_output_iou_coef: 0.8835 - val_boundary_output_iou_coef: 0.7792\n", "Epoch 233/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2928 - mask_output_loss: 0.1686 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8273 - boundary_output_iou_coef: 0.7024\n", "Epoch 233: val_loss improved from 0.19503 to 0.19431, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 549s 2s/step - loss: 0.2928 - mask_output_loss: 0.1686 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8273 - boundary_output_iou_coef: 0.7024 - val_loss: 0.1943 - val_mask_output_loss: 0.1065 - val_boundary_output_loss: 0.1756 - val_mask_output_iou_coef: 0.8878 - val_boundary_output_iou_coef: 0.7875\n", "Epoch 234/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3006 - mask_output_loss: 0.1732 - boundary_output_loss: 0.2550 - mask_output_iou_coef: 0.8225 - boundary_output_iou_coef: 0.6950\n", "Epoch 234: val_loss did not improve from 0.19431\n", "230/230 [==============================] - 552s 2s/step - loss: 0.3006 - mask_output_loss: 0.1732 - boundary_output_loss: 0.2550 - mask_output_iou_coef: 0.8225 - boundary_output_iou_coef: 0.6950 - val_loss: 0.1967 - val_mask_output_loss: 0.1084 - val_boundary_output_loss: 0.1766 - val_mask_output_iou_coef: 0.8873 - val_boundary_output_iou_coef: 0.7868\n", "Epoch 235/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3023 - mask_output_loss: 0.1763 - boundary_output_loss: 0.2521 - mask_output_iou_coef: 0.8201 - boundary_output_iou_coef: 0.6984\n", "Epoch 235: val_loss did not improve from 0.19431\n", "230/230 [==============================] - 549s 2s/step - loss: 0.3023 - mask_output_loss: 0.1763 - boundary_output_loss: 0.2521 - mask_output_iou_coef: 0.8201 - boundary_output_iou_coef: 0.6984 - val_loss: 0.1973 - val_mask_output_loss: 0.1075 - val_boundary_output_loss: 0.1796 - val_mask_output_iou_coef: 0.8849 - val_boundary_output_iou_coef: 0.7820\n", "Epoch 236/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3041 - mask_output_loss: 0.1774 - boundary_output_loss: 0.2535 - mask_output_iou_coef: 0.8193 - boundary_output_iou_coef: 0.6963\n", "Epoch 236: val_loss did not improve from 0.19431\n", "230/230 [==============================] - 550s 2s/step - loss: 0.3041 - mask_output_loss: 0.1774 - boundary_output_loss: 0.2535 - mask_output_iou_coef: 0.8193 - boundary_output_iou_coef: 0.6963 - val_loss: 0.2009 - val_mask_output_loss: 0.1104 - val_boundary_output_loss: 0.1811 - val_mask_output_iou_coef: 0.8846 - val_boundary_output_iou_coef: 0.7824\n", "Epoch 237/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2895 - mask_output_loss: 0.1659 - boundary_output_loss: 0.2473 - mask_output_iou_coef: 0.8292 - boundary_output_iou_coef: 0.7040\n", "Epoch 237: val_loss did not improve from 0.19431\n", "230/230 [==============================] - 547s 2s/step - loss: 0.2895 - mask_output_loss: 0.1659 - boundary_output_loss: 0.2473 - mask_output_iou_coef: 0.8292 - boundary_output_iou_coef: 0.7040 - val_loss: 0.1972 - val_mask_output_loss: 0.1083 - val_boundary_output_loss: 0.1776 - val_mask_output_iou_coef: 0.8874 - val_boundary_output_iou_coef: 0.7881\n", "Epoch 238/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2966 - mask_output_loss: 0.1704 - boundary_output_loss: 0.2526 - mask_output_iou_coef: 0.8254 - boundary_output_iou_coef: 0.6975\n", "Epoch 238: val_loss did not improve from 0.19431\n", "230/230 [==============================] - 556s 2s/step - loss: 0.2966 - mask_output_loss: 0.1704 - boundary_output_loss: 0.2526 - mask_output_iou_coef: 0.8254 - boundary_output_iou_coef: 0.6975 - val_loss: 0.2022 - val_mask_output_loss: 0.1110 - val_boundary_output_loss: 0.1823 - val_mask_output_iou_coef: 0.8870 - val_boundary_output_iou_coef: 0.7812\n", "Epoch 239/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2908 - mask_output_loss: 0.1654 - boundary_output_loss: 0.2508 - mask_output_iou_coef: 0.8298 - boundary_output_iou_coef: 0.6996\n", "Epoch 239: val_loss did not improve from 0.19431\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2908 - mask_output_loss: 0.1654 - boundary_output_loss: 0.2508 - mask_output_iou_coef: 0.8298 - boundary_output_iou_coef: 0.6996 - val_loss: 0.1995 - val_mask_output_loss: 0.1095 - val_boundary_output_loss: 0.1800 - val_mask_output_iou_coef: 0.8853 - val_boundary_output_iou_coef: 0.7839\n", "Epoch 240/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2917 - mask_output_loss: 0.1643 - boundary_output_loss: 0.2546 - mask_output_iou_coef: 0.8290 - boundary_output_iou_coef: 0.6962\n", "Epoch 240: val_loss did not improve from 0.19431\n", "230/230 [==============================] - 550s 2s/step - loss: 0.2917 - mask_output_loss: 0.1643 - boundary_output_loss: 0.2546 - mask_output_iou_coef: 0.8290 - boundary_output_iou_coef: 0.6962 - val_loss: 0.1985 - val_mask_output_loss: 0.1086 - val_boundary_output_loss: 0.1797 - val_mask_output_iou_coef: 0.8869 - val_boundary_output_iou_coef: 0.7841\n", "Epoch 241/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2900 - mask_output_loss: 0.1650 - boundary_output_loss: 0.2500 - mask_output_iou_coef: 0.8303 - boundary_output_iou_coef: 0.7013\n", "Epoch 241: val_loss did not improve from 0.19431\n", "230/230 [==============================] - 525s 2s/step - loss: 0.2900 - mask_output_loss: 0.1650 - boundary_output_loss: 0.2500 - mask_output_iou_coef: 0.8303 - boundary_output_iou_coef: 0.7013 - val_loss: 0.1980 - val_mask_output_loss: 0.1092 - val_boundary_output_loss: 0.1777 - val_mask_output_iou_coef: 0.8876 - val_boundary_output_iou_coef: 0.7877\n", "Epoch 242/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2945 - mask_output_loss: 0.1684 - boundary_output_loss: 0.2523 - mask_output_iou_coef: 0.8265 - boundary_output_iou_coef: 0.6971\n", "Epoch 242: val_loss improved from 0.19431 to 0.19349, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 534s 2s/step - loss: 0.2945 - mask_output_loss: 0.1684 - boundary_output_loss: 0.2523 - mask_output_iou_coef: 0.8265 - boundary_output_iou_coef: 0.6971 - val_loss: 0.1935 - val_mask_output_loss: 0.1059 - val_boundary_output_loss: 0.1752 - val_mask_output_iou_coef: 0.8890 - val_boundary_output_iou_coef: 0.7884\n", "Epoch 243/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2868 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2478 - mask_output_iou_coef: 0.8310 - boundary_output_iou_coef: 0.7029\n", "Epoch 243: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 537s 2s/step - loss: 0.2868 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2478 - mask_output_iou_coef: 0.8310 - boundary_output_iou_coef: 0.7029 - val_loss: 0.2017 - val_mask_output_loss: 0.1102 - val_boundary_output_loss: 0.1830 - val_mask_output_iou_coef: 0.8860 - val_boundary_output_iou_coef: 0.7844\n", "Epoch 244/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2966 - mask_output_loss: 0.1706 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8243 - boundary_output_iou_coef: 0.6988\n", "Epoch 244: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 539s 2s/step - loss: 0.2966 - mask_output_loss: 0.1706 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8243 - boundary_output_iou_coef: 0.6988 - val_loss: 0.1976 - val_mask_output_loss: 0.1086 - val_boundary_output_loss: 0.1781 - val_mask_output_iou_coef: 0.8867 - val_boundary_output_iou_coef: 0.7841\n", "Epoch 245/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2872 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8313 - boundary_output_iou_coef: 0.7025\n", "Epoch 245: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2872 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8313 - boundary_output_iou_coef: 0.7025 - val_loss: 0.2071 - val_mask_output_loss: 0.1136 - val_boundary_output_loss: 0.1869 - val_mask_output_iou_coef: 0.8796 - val_boundary_output_iou_coef: 0.7779\n", "Epoch 246/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2979 - mask_output_loss: 0.1722 - boundary_output_loss: 0.2513 - mask_output_iou_coef: 0.8231 - boundary_output_iou_coef: 0.6993\n", "Epoch 246: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2979 - mask_output_loss: 0.1722 - boundary_output_loss: 0.2513 - mask_output_iou_coef: 0.8231 - boundary_output_iou_coef: 0.6993 - val_loss: 0.1975 - val_mask_output_loss: 0.1095 - val_boundary_output_loss: 0.1760 - val_mask_output_iou_coef: 0.8876 - val_boundary_output_iou_coef: 0.7884\n", "Epoch 247/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2980 - mask_output_loss: 0.1720 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8238 - boundary_output_iou_coef: 0.6981\n", "Epoch 247: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2980 - mask_output_loss: 0.1720 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8238 - boundary_output_iou_coef: 0.6981 - val_loss: 0.2018 - val_mask_output_loss: 0.1121 - val_boundary_output_loss: 0.1796 - val_mask_output_iou_coef: 0.8863 - val_boundary_output_iou_coef: 0.7864\n", "Epoch 248/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3027 - mask_output_loss: 0.1758 - boundary_output_loss: 0.2539 - mask_output_iou_coef: 0.8217 - boundary_output_iou_coef: 0.6964\n", "Epoch 248: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 546s 2s/step - loss: 0.3027 - mask_output_loss: 0.1758 - boundary_output_loss: 0.2539 - mask_output_iou_coef: 0.8217 - boundary_output_iou_coef: 0.6964 - val_loss: 0.2034 - val_mask_output_loss: 0.1116 - val_boundary_output_loss: 0.1836 - val_mask_output_iou_coef: 0.8807 - val_boundary_output_iou_coef: 0.7828\n", "Epoch 249/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2946 - mask_output_loss: 0.1687 - boundary_output_loss: 0.2516 - mask_output_iou_coef: 0.8256 - boundary_output_iou_coef: 0.6991\n", "Epoch 249: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2946 - mask_output_loss: 0.1687 - boundary_output_loss: 0.2516 - mask_output_iou_coef: 0.8256 - boundary_output_iou_coef: 0.6991 - val_loss: 0.1988 - val_mask_output_loss: 0.1086 - val_boundary_output_loss: 0.1805 - val_mask_output_iou_coef: 0.8883 - val_boundary_output_iou_coef: 0.7842\n", "Epoch 250/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2989 - mask_output_loss: 0.1727 - boundary_output_loss: 0.2524 - mask_output_iou_coef: 0.8224 - boundary_output_iou_coef: 0.6978\n", "Epoch 250: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 532s 2s/step - loss: 0.2989 - mask_output_loss: 0.1727 - boundary_output_loss: 0.2524 - mask_output_iou_coef: 0.8224 - boundary_output_iou_coef: 0.6978 - val_loss: 0.2023 - val_mask_output_loss: 0.1119 - val_boundary_output_loss: 0.1808 - val_mask_output_iou_coef: 0.8861 - val_boundary_output_iou_coef: 0.7823\n", "Epoch 251/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2924 - mask_output_loss: 0.1670 - boundary_output_loss: 0.2507 - mask_output_iou_coef: 0.8279 - boundary_output_iou_coef: 0.6998\n", "Epoch 251: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2924 - mask_output_loss: 0.1670 - boundary_output_loss: 0.2507 - mask_output_iou_coef: 0.8279 - boundary_output_iou_coef: 0.6998 - val_loss: 0.1961 - val_mask_output_loss: 0.1084 - val_boundary_output_loss: 0.1755 - val_mask_output_iou_coef: 0.8876 - val_boundary_output_iou_coef: 0.7880\n", "Epoch 252/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2995 - mask_output_loss: 0.1731 - boundary_output_loss: 0.2530 - mask_output_iou_coef: 0.8232 - boundary_output_iou_coef: 0.6970\n", "Epoch 252: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2995 - mask_output_loss: 0.1731 - boundary_output_loss: 0.2530 - mask_output_iou_coef: 0.8232 - boundary_output_iou_coef: 0.6970 - val_loss: 0.1999 - val_mask_output_loss: 0.1097 - val_boundary_output_loss: 0.1803 - val_mask_output_iou_coef: 0.8829 - val_boundary_output_iou_coef: 0.7825\n", "Epoch 253/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2967 - mask_output_loss: 0.1710 - boundary_output_loss: 0.2513 - mask_output_iou_coef: 0.8231 - boundary_output_iou_coef: 0.6985\n", "Epoch 253: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2967 - mask_output_loss: 0.1710 - boundary_output_loss: 0.2513 - mask_output_iou_coef: 0.8231 - boundary_output_iou_coef: 0.6985 - val_loss: 0.1982 - val_mask_output_loss: 0.1081 - val_boundary_output_loss: 0.1803 - val_mask_output_iou_coef: 0.8875 - val_boundary_output_iou_coef: 0.7849\n", "Epoch 254/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3061 - mask_output_loss: 0.1791 - boundary_output_loss: 0.2540 - mask_output_iou_coef: 0.8199 - boundary_output_iou_coef: 0.6966\n", "Epoch 254: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 541s 2s/step - loss: 0.3061 - mask_output_loss: 0.1791 - boundary_output_loss: 0.2540 - mask_output_iou_coef: 0.8199 - boundary_output_iou_coef: 0.6966 - val_loss: 0.1965 - val_mask_output_loss: 0.1080 - val_boundary_output_loss: 0.1770 - val_mask_output_iou_coef: 0.8881 - val_boundary_output_iou_coef: 0.7874\n", "Epoch 255/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2977 - mask_output_loss: 0.1733 - boundary_output_loss: 0.2489 - mask_output_iou_coef: 0.8225 - boundary_output_iou_coef: 0.7014\n", "Epoch 255: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2977 - mask_output_loss: 0.1733 - boundary_output_loss: 0.2489 - mask_output_iou_coef: 0.8225 - boundary_output_iou_coef: 0.7014 - val_loss: 0.1977 - val_mask_output_loss: 0.1083 - val_boundary_output_loss: 0.1788 - val_mask_output_iou_coef: 0.8884 - val_boundary_output_iou_coef: 0.7876\n", "Epoch 256/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3004 - mask_output_loss: 0.1722 - boundary_output_loss: 0.2564 - mask_output_iou_coef: 0.8224 - boundary_output_iou_coef: 0.6945\n", "Epoch 256: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 538s 2s/step - loss: 0.3004 - mask_output_loss: 0.1722 - boundary_output_loss: 0.2564 - mask_output_iou_coef: 0.8224 - boundary_output_iou_coef: 0.6945 - val_loss: 0.1979 - val_mask_output_loss: 0.1060 - val_boundary_output_loss: 0.1838 - val_mask_output_iou_coef: 0.8902 - val_boundary_output_iou_coef: 0.7849\n", "Epoch 257/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2998 - mask_output_loss: 0.1719 - boundary_output_loss: 0.2558 - mask_output_iou_coef: 0.8248 - boundary_output_iou_coef: 0.6944\n", "Epoch 257: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2998 - mask_output_loss: 0.1719 - boundary_output_loss: 0.2558 - mask_output_iou_coef: 0.8248 - boundary_output_iou_coef: 0.6944 - val_loss: 0.2019 - val_mask_output_loss: 0.1117 - val_boundary_output_loss: 0.1803 - val_mask_output_iou_coef: 0.8823 - val_boundary_output_iou_coef: 0.7848\n", "Epoch 258/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2920 - mask_output_loss: 0.1688 - boundary_output_loss: 0.2464 - mask_output_iou_coef: 0.8256 - boundary_output_iou_coef: 0.7042\n", "Epoch 258: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 537s 2s/step - loss: 0.2920 - mask_output_loss: 0.1688 - boundary_output_loss: 0.2464 - mask_output_iou_coef: 0.8256 - boundary_output_iou_coef: 0.7042 - val_loss: 0.1948 - val_mask_output_loss: 0.1065 - val_boundary_output_loss: 0.1767 - val_mask_output_iou_coef: 0.8885 - val_boundary_output_iou_coef: 0.7879\n", "Epoch 259/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2920 - mask_output_loss: 0.1660 - boundary_output_loss: 0.2520 - mask_output_iou_coef: 0.8285 - boundary_output_iou_coef: 0.6995\n", "Epoch 259: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 539s 2s/step - loss: 0.2920 - mask_output_loss: 0.1660 - boundary_output_loss: 0.2520 - mask_output_iou_coef: 0.8285 - boundary_output_iou_coef: 0.6995 - val_loss: 0.1945 - val_mask_output_loss: 0.1066 - val_boundary_output_loss: 0.1757 - val_mask_output_iou_coef: 0.8884 - val_boundary_output_iou_coef: 0.7873\n", "Epoch 260/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2921 - mask_output_loss: 0.1667 - boundary_output_loss: 0.2508 - mask_output_iou_coef: 0.8282 - boundary_output_iou_coef: 0.6996\n", "Epoch 260: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 539s 2s/step - loss: 0.2921 - mask_output_loss: 0.1667 - boundary_output_loss: 0.2508 - mask_output_iou_coef: 0.8282 - boundary_output_iou_coef: 0.6996 - val_loss: 0.1956 - val_mask_output_loss: 0.1067 - val_boundary_output_loss: 0.1778 - val_mask_output_iou_coef: 0.8871 - val_boundary_output_iou_coef: 0.7874\n", "Epoch 261/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2880 - mask_output_loss: 0.1631 - boundary_output_loss: 0.2498 - mask_output_iou_coef: 0.8295 - boundary_output_iou_coef: 0.7009\n", "Epoch 261: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2880 - mask_output_loss: 0.1631 - boundary_output_loss: 0.2498 - mask_output_iou_coef: 0.8295 - boundary_output_iou_coef: 0.7009 - val_loss: 0.1957 - val_mask_output_loss: 0.1072 - val_boundary_output_loss: 0.1769 - val_mask_output_iou_coef: 0.8872 - val_boundary_output_iou_coef: 0.7862\n", "Epoch 262/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2910 - mask_output_loss: 0.1663 - boundary_output_loss: 0.2493 - mask_output_iou_coef: 0.8288 - boundary_output_iou_coef: 0.7016\n", "Epoch 262: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2910 - mask_output_loss: 0.1663 - boundary_output_loss: 0.2493 - mask_output_iou_coef: 0.8288 - boundary_output_iou_coef: 0.7016 - val_loss: 0.1967 - val_mask_output_loss: 0.1083 - val_boundary_output_loss: 0.1767 - val_mask_output_iou_coef: 0.8867 - val_boundary_output_iou_coef: 0.7869\n", "Epoch 263/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2917 - mask_output_loss: 0.1651 - boundary_output_loss: 0.2531 - mask_output_iou_coef: 0.8279 - boundary_output_iou_coef: 0.6974\n", "Epoch 263: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 555s 2s/step - loss: 0.2917 - mask_output_loss: 0.1651 - boundary_output_loss: 0.2531 - mask_output_iou_coef: 0.8279 - boundary_output_iou_coef: 0.6974 - val_loss: 0.1936 - val_mask_output_loss: 0.1060 - val_boundary_output_loss: 0.1753 - val_mask_output_iou_coef: 0.8905 - val_boundary_output_iou_coef: 0.7912\n", "Epoch 264/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2870 - mask_output_loss: 0.1626 - boundary_output_loss: 0.2490 - mask_output_iou_coef: 0.8326 - boundary_output_iou_coef: 0.7015\n", "Epoch 264: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2870 - mask_output_loss: 0.1626 - boundary_output_loss: 0.2490 - mask_output_iou_coef: 0.8326 - boundary_output_iou_coef: 0.7015 - val_loss: 0.2031 - val_mask_output_loss: 0.1125 - val_boundary_output_loss: 0.1813 - val_mask_output_iou_coef: 0.8831 - val_boundary_output_iou_coef: 0.7853\n", "Epoch 265/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2835 - mask_output_loss: 0.1602 - boundary_output_loss: 0.2466 - mask_output_iou_coef: 0.8338 - boundary_output_iou_coef: 0.7045\n", "Epoch 265: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 554s 2s/step - loss: 0.2835 - mask_output_loss: 0.1602 - boundary_output_loss: 0.2466 - mask_output_iou_coef: 0.8338 - boundary_output_iou_coef: 0.7045 - val_loss: 0.1938 - val_mask_output_loss: 0.1058 - val_boundary_output_loss: 0.1761 - val_mask_output_iou_coef: 0.8910 - val_boundary_output_iou_coef: 0.7900\n", "Epoch 266/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2987 - mask_output_loss: 0.1711 - boundary_output_loss: 0.2552 - mask_output_iou_coef: 0.8237 - boundary_output_iou_coef: 0.6943\n", "Epoch 266: val_loss did not improve from 0.19349\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2987 - mask_output_loss: 0.1711 - boundary_output_loss: 0.2552 - mask_output_iou_coef: 0.8237 - boundary_output_iou_coef: 0.6943 - val_loss: 0.1987 - val_mask_output_loss: 0.1088 - val_boundary_output_loss: 0.1798 - val_mask_output_iou_coef: 0.8811 - val_boundary_output_iou_coef: 0.7830\n", "Epoch 267/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2983 - mask_output_loss: 0.1732 - boundary_output_loss: 0.2502 - mask_output_iou_coef: 0.8232 - boundary_output_iou_coef: 0.7003\n", "Epoch 267: val_loss improved from 0.19349 to 0.19189, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 554s 2s/step - loss: 0.2983 - mask_output_loss: 0.1732 - boundary_output_loss: 0.2502 - mask_output_iou_coef: 0.8232 - boundary_output_iou_coef: 0.7003 - val_loss: 0.1919 - val_mask_output_loss: 0.1046 - val_boundary_output_loss: 0.1747 - val_mask_output_iou_coef: 0.8883 - val_boundary_output_iou_coef: 0.7875\n", "Epoch 268/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2976 - mask_output_loss: 0.1720 - boundary_output_loss: 0.2512 - mask_output_iou_coef: 0.8237 - boundary_output_iou_coef: 0.6987\n", "Epoch 268: val_loss did not improve from 0.19189\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2976 - mask_output_loss: 0.1720 - boundary_output_loss: 0.2512 - mask_output_iou_coef: 0.8237 - boundary_output_iou_coef: 0.6987 - val_loss: 0.2011 - val_mask_output_loss: 0.1100 - val_boundary_output_loss: 0.1822 - val_mask_output_iou_coef: 0.8872 - val_boundary_output_iou_coef: 0.7856\n", "Epoch 269/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2846 - mask_output_loss: 0.1594 - boundary_output_loss: 0.2505 - mask_output_iou_coef: 0.8341 - boundary_output_iou_coef: 0.6996\n", "Epoch 269: val_loss did not improve from 0.19189\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2846 - mask_output_loss: 0.1594 - boundary_output_loss: 0.2505 - mask_output_iou_coef: 0.8341 - boundary_output_iou_coef: 0.6996 - val_loss: 0.1980 - val_mask_output_loss: 0.1084 - val_boundary_output_loss: 0.1792 - val_mask_output_iou_coef: 0.8883 - val_boundary_output_iou_coef: 0.7857\n", "Epoch 270/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2917 - mask_output_loss: 0.1685 - boundary_output_loss: 0.2465 - mask_output_iou_coef: 0.8260 - boundary_output_iou_coef: 0.7037\n", "Epoch 270: val_loss improved from 0.19189 to 0.19188, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2917 - mask_output_loss: 0.1685 - boundary_output_loss: 0.2465 - mask_output_iou_coef: 0.8260 - boundary_output_iou_coef: 0.7037 - val_loss: 0.1919 - val_mask_output_loss: 0.1050 - val_boundary_output_loss: 0.1737 - val_mask_output_iou_coef: 0.8876 - val_boundary_output_iou_coef: 0.7905\n", "Epoch 271/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2963 - mask_output_loss: 0.1704 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8246 - boundary_output_iou_coef: 0.6984\n", "Epoch 271: val_loss did not improve from 0.19188\n", "230/230 [==============================] - 556s 2s/step - loss: 0.2963 - mask_output_loss: 0.1704 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8246 - boundary_output_iou_coef: 0.6984 - val_loss: 0.2022 - val_mask_output_loss: 0.1111 - val_boundary_output_loss: 0.1823 - val_mask_output_iou_coef: 0.8858 - val_boundary_output_iou_coef: 0.7827\n", "Epoch 272/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2913 - mask_output_loss: 0.1670 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8277 - boundary_output_iou_coef: 0.7021\n", "Epoch 272: val_loss did not improve from 0.19188\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2913 - mask_output_loss: 0.1670 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8277 - boundary_output_iou_coef: 0.7021 - val_loss: 0.1962 - val_mask_output_loss: 0.1076 - val_boundary_output_loss: 0.1772 - val_mask_output_iou_coef: 0.8851 - val_boundary_output_iou_coef: 0.7842\n", "Epoch 273/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2849 - mask_output_loss: 0.1623 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8315 - boundary_output_iou_coef: 0.7057\n", "Epoch 273: val_loss did not improve from 0.19188\n", "230/230 [==============================] - 561s 2s/step - loss: 0.2849 - mask_output_loss: 0.1623 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8315 - boundary_output_iou_coef: 0.7057 - val_loss: 0.1953 - val_mask_output_loss: 0.1063 - val_boundary_output_loss: 0.1780 - val_mask_output_iou_coef: 0.8892 - val_boundary_output_iou_coef: 0.7870\n", "Epoch 274/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2819 - mask_output_loss: 0.1606 - boundary_output_loss: 0.2425 - mask_output_iou_coef: 0.8326 - boundary_output_iou_coef: 0.7080\n", "Epoch 274: val_loss did not improve from 0.19188\n", "230/230 [==============================] - 554s 2s/step - loss: 0.2819 - mask_output_loss: 0.1606 - boundary_output_loss: 0.2425 - mask_output_iou_coef: 0.8326 - boundary_output_iou_coef: 0.7080 - val_loss: 0.1941 - val_mask_output_loss: 0.1058 - val_boundary_output_loss: 0.1767 - val_mask_output_iou_coef: 0.8914 - val_boundary_output_iou_coef: 0.7882\n", "Epoch 275/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2891 - mask_output_loss: 0.1665 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8295 - boundary_output_iou_coef: 0.7054\n", "Epoch 275: val_loss improved from 0.19188 to 0.19059, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2891 - mask_output_loss: 0.1665 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8295 - boundary_output_iou_coef: 0.7054 - val_loss: 0.1906 - val_mask_output_loss: 0.1040 - val_boundary_output_loss: 0.1732 - val_mask_output_iou_coef: 0.8884 - val_boundary_output_iou_coef: 0.7901\n", "Epoch 276/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3053 - mask_output_loss: 0.1762 - boundary_output_loss: 0.2581 - mask_output_iou_coef: 0.8196 - boundary_output_iou_coef: 0.6922\n", "Epoch 276: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 546s 2s/step - loss: 0.3053 - mask_output_loss: 0.1762 - boundary_output_loss: 0.2581 - mask_output_iou_coef: 0.8196 - boundary_output_iou_coef: 0.6922 - val_loss: 0.2034 - val_mask_output_loss: 0.1100 - val_boundary_output_loss: 0.1868 - val_mask_output_iou_coef: 0.8864 - val_boundary_output_iou_coef: 0.7785\n", "Epoch 277/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2922 - mask_output_loss: 0.1667 - boundary_output_loss: 0.2510 - mask_output_iou_coef: 0.8282 - boundary_output_iou_coef: 0.6989\n", "Epoch 277: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2922 - mask_output_loss: 0.1667 - boundary_output_loss: 0.2510 - mask_output_iou_coef: 0.8282 - boundary_output_iou_coef: 0.6989 - val_loss: 0.1954 - val_mask_output_loss: 0.1061 - val_boundary_output_loss: 0.1785 - val_mask_output_iou_coef: 0.8923 - val_boundary_output_iou_coef: 0.7867\n", "Epoch 278/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2901 - mask_output_loss: 0.1651 - boundary_output_loss: 0.2500 - mask_output_iou_coef: 0.8294 - boundary_output_iou_coef: 0.7003\n", "Epoch 278: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2901 - mask_output_loss: 0.1651 - boundary_output_loss: 0.2500 - mask_output_iou_coef: 0.8294 - boundary_output_iou_coef: 0.7003 - val_loss: 0.1984 - val_mask_output_loss: 0.1085 - val_boundary_output_loss: 0.1799 - val_mask_output_iou_coef: 0.8863 - val_boundary_output_iou_coef: 0.7832\n", "Epoch 279/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2898 - mask_output_loss: 0.1664 - boundary_output_loss: 0.2469 - mask_output_iou_coef: 0.8287 - boundary_output_iou_coef: 0.7040\n", "Epoch 279: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 559s 2s/step - loss: 0.2898 - mask_output_loss: 0.1664 - boundary_output_loss: 0.2469 - mask_output_iou_coef: 0.8287 - boundary_output_iou_coef: 0.7040 - val_loss: 0.1969 - val_mask_output_loss: 0.1081 - val_boundary_output_loss: 0.1776 - val_mask_output_iou_coef: 0.8836 - val_boundary_output_iou_coef: 0.7841\n", "Epoch 280/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2932 - mask_output_loss: 0.1667 - boundary_output_loss: 0.2531 - mask_output_iou_coef: 0.8273 - boundary_output_iou_coef: 0.6972\n", "Epoch 280: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 555s 2s/step - loss: 0.2932 - mask_output_loss: 0.1667 - boundary_output_loss: 0.2531 - mask_output_iou_coef: 0.8273 - boundary_output_iou_coef: 0.6972 - val_loss: 0.2075 - val_mask_output_loss: 0.1145 - val_boundary_output_loss: 0.1860 - val_mask_output_iou_coef: 0.8824 - val_boundary_output_iou_coef: 0.7798\n", "Epoch 281/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2992 - mask_output_loss: 0.1731 - boundary_output_loss: 0.2522 - mask_output_iou_coef: 0.8231 - boundary_output_iou_coef: 0.6981\n", "Epoch 281: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 558s 2s/step - loss: 0.2992 - mask_output_loss: 0.1731 - boundary_output_loss: 0.2522 - mask_output_iou_coef: 0.8231 - boundary_output_iou_coef: 0.6981 - val_loss: 0.1962 - val_mask_output_loss: 0.1075 - val_boundary_output_loss: 0.1774 - val_mask_output_iou_coef: 0.8875 - val_boundary_output_iou_coef: 0.7852\n", "Epoch 282/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2907 - mask_output_loss: 0.1661 - boundary_output_loss: 0.2492 - mask_output_iou_coef: 0.8290 - boundary_output_iou_coef: 0.7009\n", "Epoch 282: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2907 - mask_output_loss: 0.1661 - boundary_output_loss: 0.2492 - mask_output_iou_coef: 0.8290 - boundary_output_iou_coef: 0.7009 - val_loss: 0.1980 - val_mask_output_loss: 0.1086 - val_boundary_output_loss: 0.1788 - val_mask_output_iou_coef: 0.8874 - val_boundary_output_iou_coef: 0.7863\n", "Epoch 283/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2947 - mask_output_loss: 0.1699 - boundary_output_loss: 0.2498 - mask_output_iou_coef: 0.8263 - boundary_output_iou_coef: 0.7005\n", "Epoch 283: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 559s 2s/step - loss: 0.2947 - mask_output_loss: 0.1699 - boundary_output_loss: 0.2498 - mask_output_iou_coef: 0.8263 - boundary_output_iou_coef: 0.7005 - val_loss: 0.2003 - val_mask_output_loss: 0.1087 - val_boundary_output_loss: 0.1831 - val_mask_output_iou_coef: 0.8846 - val_boundary_output_iou_coef: 0.7819\n", "Epoch 284/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2828 - mask_output_loss: 0.1597 - boundary_output_loss: 0.2462 - mask_output_iou_coef: 0.8339 - boundary_output_iou_coef: 0.7047\n", "Epoch 284: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 562s 2s/step - loss: 0.2828 - mask_output_loss: 0.1597 - boundary_output_loss: 0.2462 - mask_output_iou_coef: 0.8339 - boundary_output_iou_coef: 0.7047 - val_loss: 0.2006 - val_mask_output_loss: 0.1108 - val_boundary_output_loss: 0.1796 - val_mask_output_iou_coef: 0.8866 - val_boundary_output_iou_coef: 0.7850\n", "Epoch 285/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2894 - mask_output_loss: 0.1660 - boundary_output_loss: 0.2469 - mask_output_iou_coef: 0.8296 - boundary_output_iou_coef: 0.7031\n", "Epoch 285: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 569s 2s/step - loss: 0.2894 - mask_output_loss: 0.1660 - boundary_output_loss: 0.2469 - mask_output_iou_coef: 0.8296 - boundary_output_iou_coef: 0.7031 - val_loss: 0.1966 - val_mask_output_loss: 0.1072 - val_boundary_output_loss: 0.1788 - val_mask_output_iou_coef: 0.8918 - val_boundary_output_iou_coef: 0.7874\n", "Epoch 286/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2874 - mask_output_loss: 0.1639 - boundary_output_loss: 0.2470 - mask_output_iou_coef: 0.8304 - boundary_output_iou_coef: 0.7038\n", "Epoch 286: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 565s 2s/step - loss: 0.2874 - mask_output_loss: 0.1639 - boundary_output_loss: 0.2470 - mask_output_iou_coef: 0.8304 - boundary_output_iou_coef: 0.7038 - val_loss: 0.1927 - val_mask_output_loss: 0.1049 - val_boundary_output_loss: 0.1756 - val_mask_output_iou_coef: 0.8908 - val_boundary_output_iou_coef: 0.7885\n", "Epoch 287/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2843 - mask_output_loss: 0.1602 - boundary_output_loss: 0.2481 - mask_output_iou_coef: 0.8345 - boundary_output_iou_coef: 0.7025\n", "Epoch 287: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 566s 2s/step - loss: 0.2843 - mask_output_loss: 0.1602 - boundary_output_loss: 0.2481 - mask_output_iou_coef: 0.8345 - boundary_output_iou_coef: 0.7025 - val_loss: 0.1987 - val_mask_output_loss: 0.1093 - val_boundary_output_loss: 0.1788 - val_mask_output_iou_coef: 0.8850 - val_boundary_output_iou_coef: 0.7860\n", "Epoch 288/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2829 - mask_output_loss: 0.1603 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8327 - boundary_output_iou_coef: 0.7062\n", "Epoch 288: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 555s 2s/step - loss: 0.2829 - mask_output_loss: 0.1603 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8327 - boundary_output_iou_coef: 0.7062 - val_loss: 0.1982 - val_mask_output_loss: 0.1089 - val_boundary_output_loss: 0.1787 - val_mask_output_iou_coef: 0.8854 - val_boundary_output_iou_coef: 0.7845\n", "Epoch 289/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2790 - mask_output_loss: 0.1571 - boundary_output_loss: 0.2439 - mask_output_iou_coef: 0.8355 - boundary_output_iou_coef: 0.7068\n", "Epoch 289: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 554s 2s/step - loss: 0.2790 - mask_output_loss: 0.1571 - boundary_output_loss: 0.2439 - mask_output_iou_coef: 0.8355 - boundary_output_iou_coef: 0.7068 - val_loss: 0.1955 - val_mask_output_loss: 0.1071 - val_boundary_output_loss: 0.1768 - val_mask_output_iou_coef: 0.8897 - val_boundary_output_iou_coef: 0.7868\n", "Epoch 290/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2896 - mask_output_loss: 0.1643 - boundary_output_loss: 0.2505 - mask_output_iou_coef: 0.8300 - boundary_output_iou_coef: 0.6998\n", "Epoch 290: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2896 - mask_output_loss: 0.1643 - boundary_output_loss: 0.2505 - mask_output_iou_coef: 0.8300 - boundary_output_iou_coef: 0.6998 - val_loss: 0.1961 - val_mask_output_loss: 0.1068 - val_boundary_output_loss: 0.1785 - val_mask_output_iou_coef: 0.8868 - val_boundary_output_iou_coef: 0.7845\n", "Epoch 291/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2951 - mask_output_loss: 0.1691 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8259 - boundary_output_iou_coef: 0.6985\n", "Epoch 291: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 545s 2s/step - loss: 0.2951 - mask_output_loss: 0.1691 - boundary_output_loss: 0.2519 - mask_output_iou_coef: 0.8259 - boundary_output_iou_coef: 0.6985 - val_loss: 0.1914 - val_mask_output_loss: 0.1047 - val_boundary_output_loss: 0.1733 - val_mask_output_iou_coef: 0.8911 - val_boundary_output_iou_coef: 0.7897\n", "Epoch 292/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2893 - mask_output_loss: 0.1650 - boundary_output_loss: 0.2485 - mask_output_iou_coef: 0.8313 - boundary_output_iou_coef: 0.7022\n", "Epoch 292: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 555s 2s/step - loss: 0.2893 - mask_output_loss: 0.1650 - boundary_output_loss: 0.2485 - mask_output_iou_coef: 0.8313 - boundary_output_iou_coef: 0.7022 - val_loss: 0.1923 - val_mask_output_loss: 0.1056 - val_boundary_output_loss: 0.1735 - val_mask_output_iou_coef: 0.8899 - val_boundary_output_iou_coef: 0.7912\n", "Epoch 293/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2939 - mask_output_loss: 0.1692 - boundary_output_loss: 0.2494 - mask_output_iou_coef: 0.8253 - boundary_output_iou_coef: 0.7015\n", "Epoch 293: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2939 - mask_output_loss: 0.1692 - boundary_output_loss: 0.2494 - mask_output_iou_coef: 0.8253 - boundary_output_iou_coef: 0.7015 - val_loss: 0.1964 - val_mask_output_loss: 0.1075 - val_boundary_output_loss: 0.1779 - val_mask_output_iou_coef: 0.8879 - val_boundary_output_iou_coef: 0.7862\n", "Epoch 294/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2847 - mask_output_loss: 0.1608 - boundary_output_loss: 0.2479 - mask_output_iou_coef: 0.8314 - boundary_output_iou_coef: 0.7021\n", "Epoch 294: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 554s 2s/step - loss: 0.2847 - mask_output_loss: 0.1608 - boundary_output_loss: 0.2479 - mask_output_iou_coef: 0.8314 - boundary_output_iou_coef: 0.7021 - val_loss: 0.1980 - val_mask_output_loss: 0.1082 - val_boundary_output_loss: 0.1796 - val_mask_output_iou_coef: 0.8902 - val_boundary_output_iou_coef: 0.7857\n", "Epoch 295/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2899 - mask_output_loss: 0.1667 - boundary_output_loss: 0.2464 - mask_output_iou_coef: 0.8279 - boundary_output_iou_coef: 0.7041\n", "Epoch 295: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2899 - mask_output_loss: 0.1667 - boundary_output_loss: 0.2464 - mask_output_iou_coef: 0.8279 - boundary_output_iou_coef: 0.7041 - val_loss: 0.1948 - val_mask_output_loss: 0.1068 - val_boundary_output_loss: 0.1761 - val_mask_output_iou_coef: 0.8889 - val_boundary_output_iou_coef: 0.7876\n", "Epoch 296/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2959 - mask_output_loss: 0.1698 - boundary_output_loss: 0.2521 - mask_output_iou_coef: 0.8256 - boundary_output_iou_coef: 0.6974\n", "Epoch 296: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 555s 2s/step - loss: 0.2959 - mask_output_loss: 0.1698 - boundary_output_loss: 0.2521 - mask_output_iou_coef: 0.8256 - boundary_output_iou_coef: 0.6974 - val_loss: 0.1929 - val_mask_output_loss: 0.1050 - val_boundary_output_loss: 0.1756 - val_mask_output_iou_coef: 0.8911 - val_boundary_output_iou_coef: 0.7905\n", "Epoch 297/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2831 - mask_output_loss: 0.1593 - boundary_output_loss: 0.2476 - mask_output_iou_coef: 0.8327 - boundary_output_iou_coef: 0.7026\n", "Epoch 297: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2831 - mask_output_loss: 0.1593 - boundary_output_loss: 0.2476 - mask_output_iou_coef: 0.8327 - boundary_output_iou_coef: 0.7026 - val_loss: 0.1946 - val_mask_output_loss: 0.1063 - val_boundary_output_loss: 0.1766 - val_mask_output_iou_coef: 0.8907 - val_boundary_output_iou_coef: 0.7868\n", "Epoch 298/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2866 - mask_output_loss: 0.1639 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8325 - boundary_output_iou_coef: 0.7051\n", "Epoch 298: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 554s 2s/step - loss: 0.2866 - mask_output_loss: 0.1639 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8325 - boundary_output_iou_coef: 0.7051 - val_loss: 0.1988 - val_mask_output_loss: 0.1096 - val_boundary_output_loss: 0.1785 - val_mask_output_iou_coef: 0.8854 - val_boundary_output_iou_coef: 0.7838\n", "Epoch 299/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2848 - mask_output_loss: 0.1616 - boundary_output_loss: 0.2465 - mask_output_iou_coef: 0.8336 - boundary_output_iou_coef: 0.7038\n", "Epoch 299: val_loss did not improve from 0.19059\n", "230/230 [==============================] - 555s 2s/step - loss: 0.2848 - mask_output_loss: 0.1616 - boundary_output_loss: 0.2465 - mask_output_iou_coef: 0.8336 - boundary_output_iou_coef: 0.7038 - val_loss: 0.1973 - val_mask_output_loss: 0.1087 - val_boundary_output_loss: 0.1772 - val_mask_output_iou_coef: 0.8890 - val_boundary_output_iou_coef: 0.7873\n", "Epoch 300/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2933 - mask_output_loss: 0.1691 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8276 - boundary_output_iou_coef: 0.7030\n", "Epoch 300: val_loss improved from 0.19059 to 0.19004, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 524s 2s/step - loss: 0.2933 - mask_output_loss: 0.1691 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8276 - boundary_output_iou_coef: 0.7030 - val_loss: 0.1900 - val_mask_output_loss: 0.1042 - val_boundary_output_loss: 0.1717 - val_mask_output_iou_coef: 0.8948 - val_boundary_output_iou_coef: 0.7948\n", "Epoch 301/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2891 - mask_output_loss: 0.1659 - boundary_output_loss: 0.2465 - mask_output_iou_coef: 0.8286 - boundary_output_iou_coef: 0.7035\n", "Epoch 301: val_loss did not improve from 0.19004\n", "230/230 [==============================] - 523s 2s/step - loss: 0.2891 - mask_output_loss: 0.1659 - boundary_output_loss: 0.2465 - mask_output_iou_coef: 0.8286 - boundary_output_iou_coef: 0.7035 - val_loss: 0.1909 - val_mask_output_loss: 0.1037 - val_boundary_output_loss: 0.1744 - val_mask_output_iou_coef: 0.8914 - val_boundary_output_iou_coef: 0.7923\n", "Epoch 302/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2816 - mask_output_loss: 0.1584 - boundary_output_loss: 0.2464 - mask_output_iou_coef: 0.8347 - boundary_output_iou_coef: 0.7043\n", "Epoch 302: val_loss improved from 0.19004 to 0.18864, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 530s 2s/step - loss: 0.2816 - mask_output_loss: 0.1584 - boundary_output_loss: 0.2464 - mask_output_iou_coef: 0.8347 - boundary_output_iou_coef: 0.7043 - val_loss: 0.1886 - val_mask_output_loss: 0.1026 - val_boundary_output_loss: 0.1721 - val_mask_output_iou_coef: 0.8926 - val_boundary_output_iou_coef: 0.7927\n", "Epoch 303/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2965 - mask_output_loss: 0.1726 - boundary_output_loss: 0.2477 - mask_output_iou_coef: 0.8258 - boundary_output_iou_coef: 0.7029\n", "Epoch 303: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 529s 2s/step - loss: 0.2965 - mask_output_loss: 0.1726 - boundary_output_loss: 0.2477 - mask_output_iou_coef: 0.8258 - boundary_output_iou_coef: 0.7029 - val_loss: 0.1959 - val_mask_output_loss: 0.1062 - val_boundary_output_loss: 0.1793 - val_mask_output_iou_coef: 0.8907 - val_boundary_output_iou_coef: 0.7883\n", "Epoch 304/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2857 - mask_output_loss: 0.1631 - boundary_output_loss: 0.2452 - mask_output_iou_coef: 0.8296 - boundary_output_iou_coef: 0.7050\n", "Epoch 304: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 533s 2s/step - loss: 0.2857 - mask_output_loss: 0.1631 - boundary_output_loss: 0.2452 - mask_output_iou_coef: 0.8296 - boundary_output_iou_coef: 0.7050 - val_loss: 0.2005 - val_mask_output_loss: 0.1096 - val_boundary_output_loss: 0.1818 - val_mask_output_iou_coef: 0.8903 - val_boundary_output_iou_coef: 0.7839\n", "Epoch 305/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2828 - mask_output_loss: 0.1600 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8344 - boundary_output_iou_coef: 0.7044\n", "Epoch 305: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 531s 2s/step - loss: 0.2828 - mask_output_loss: 0.1600 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8344 - boundary_output_iou_coef: 0.7044 - val_loss: 0.1890 - val_mask_output_loss: 0.1029 - val_boundary_output_loss: 0.1723 - val_mask_output_iou_coef: 0.8923 - val_boundary_output_iou_coef: 0.7927\n", "Epoch 306/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2825 - mask_output_loss: 0.1609 - boundary_output_loss: 0.2434 - mask_output_iou_coef: 0.8319 - boundary_output_iou_coef: 0.7072\n", "Epoch 306: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 532s 2s/step - loss: 0.2825 - mask_output_loss: 0.1609 - boundary_output_loss: 0.2434 - mask_output_iou_coef: 0.8319 - boundary_output_iou_coef: 0.7072 - val_loss: 0.1933 - val_mask_output_loss: 0.1059 - val_boundary_output_loss: 0.1749 - val_mask_output_iou_coef: 0.8903 - val_boundary_output_iou_coef: 0.7896\n", "Epoch 307/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2849 - mask_output_loss: 0.1605 - boundary_output_loss: 0.2488 - mask_output_iou_coef: 0.8323 - boundary_output_iou_coef: 0.7020\n", "Epoch 307: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 533s 2s/step - loss: 0.2849 - mask_output_loss: 0.1605 - boundary_output_loss: 0.2488 - mask_output_iou_coef: 0.8323 - boundary_output_iou_coef: 0.7020 - val_loss: 0.1970 - val_mask_output_loss: 0.1091 - val_boundary_output_loss: 0.1757 - val_mask_output_iou_coef: 0.8889 - val_boundary_output_iou_coef: 0.7896\n", "Epoch 308/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2890 - mask_output_loss: 0.1651 - boundary_output_loss: 0.2478 - mask_output_iou_coef: 0.8299 - boundary_output_iou_coef: 0.7027\n", "Epoch 308: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 533s 2s/step - loss: 0.2890 - mask_output_loss: 0.1651 - boundary_output_loss: 0.2478 - mask_output_iou_coef: 0.8299 - boundary_output_iou_coef: 0.7027 - val_loss: 0.1911 - val_mask_output_loss: 0.1033 - val_boundary_output_loss: 0.1756 - val_mask_output_iou_coef: 0.8908 - val_boundary_output_iou_coef: 0.7842\n", "Epoch 309/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2940 - mask_output_loss: 0.1699 - boundary_output_loss: 0.2482 - mask_output_iou_coef: 0.8271 - boundary_output_iou_coef: 0.7025\n", "Epoch 309: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 533s 2s/step - loss: 0.2940 - mask_output_loss: 0.1699 - boundary_output_loss: 0.2482 - mask_output_iou_coef: 0.8271 - boundary_output_iou_coef: 0.7025 - val_loss: 0.1931 - val_mask_output_loss: 0.1058 - val_boundary_output_loss: 0.1746 - val_mask_output_iou_coef: 0.8889 - val_boundary_output_iou_coef: 0.7898\n", "Epoch 310/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3109 - mask_output_loss: 0.1816 - boundary_output_loss: 0.2586 - mask_output_iou_coef: 0.8173 - boundary_output_iou_coef: 0.6933\n", "Epoch 310: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 534s 2s/step - loss: 0.3109 - mask_output_loss: 0.1816 - boundary_output_loss: 0.2586 - mask_output_iou_coef: 0.8173 - boundary_output_iou_coef: 0.6933 - val_loss: 0.1994 - val_mask_output_loss: 0.1104 - val_boundary_output_loss: 0.1780 - val_mask_output_iou_coef: 0.8858 - val_boundary_output_iou_coef: 0.7856\n", "Epoch 311/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2859 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2477 - mask_output_iou_coef: 0.8322 - boundary_output_iou_coef: 0.7023\n", "Epoch 311: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 534s 2s/step - loss: 0.2859 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2477 - mask_output_iou_coef: 0.8322 - boundary_output_iou_coef: 0.7023 - val_loss: 0.1973 - val_mask_output_loss: 0.1076 - val_boundary_output_loss: 0.1795 - val_mask_output_iou_coef: 0.8905 - val_boundary_output_iou_coef: 0.7840\n", "Epoch 312/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2796 - mask_output_loss: 0.1576 - boundary_output_loss: 0.2440 - mask_output_iou_coef: 0.8361 - boundary_output_iou_coef: 0.7066\n", "Epoch 312: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 536s 2s/step - loss: 0.2796 - mask_output_loss: 0.1576 - boundary_output_loss: 0.2440 - mask_output_iou_coef: 0.8361 - boundary_output_iou_coef: 0.7066 - val_loss: 0.1915 - val_mask_output_loss: 0.1031 - val_boundary_output_loss: 0.1767 - val_mask_output_iou_coef: 0.8939 - val_boundary_output_iou_coef: 0.7893\n", "Epoch 313/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2835 - mask_output_loss: 0.1604 - boundary_output_loss: 0.2462 - mask_output_iou_coef: 0.8338 - boundary_output_iou_coef: 0.7037\n", "Epoch 313: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 537s 2s/step - loss: 0.2835 - mask_output_loss: 0.1604 - boundary_output_loss: 0.2462 - mask_output_iou_coef: 0.8338 - boundary_output_iou_coef: 0.7037 - val_loss: 0.1918 - val_mask_output_loss: 0.1051 - val_boundary_output_loss: 0.1733 - val_mask_output_iou_coef: 0.8908 - val_boundary_output_iou_coef: 0.7903\n", "Epoch 314/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2897 - mask_output_loss: 0.1671 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8295 - boundary_output_iou_coef: 0.7053\n", "Epoch 314: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2897 - mask_output_loss: 0.1671 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8295 - boundary_output_iou_coef: 0.7053 - val_loss: 0.1918 - val_mask_output_loss: 0.1048 - val_boundary_output_loss: 0.1739 - val_mask_output_iou_coef: 0.8898 - val_boundary_output_iou_coef: 0.7901\n", "Epoch 315/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2851 - mask_output_loss: 0.1618 - boundary_output_loss: 0.2466 - mask_output_iou_coef: 0.8327 - boundary_output_iou_coef: 0.7040\n", "Epoch 315: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 520s 2s/step - loss: 0.2851 - mask_output_loss: 0.1618 - boundary_output_loss: 0.2466 - mask_output_iou_coef: 0.8327 - boundary_output_iou_coef: 0.7040 - val_loss: 0.1930 - val_mask_output_loss: 0.1059 - val_boundary_output_loss: 0.1743 - val_mask_output_iou_coef: 0.8905 - val_boundary_output_iou_coef: 0.7915\n", "Epoch 316/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2866 - mask_output_loss: 0.1645 - boundary_output_loss: 0.2443 - mask_output_iou_coef: 0.8304 - boundary_output_iou_coef: 0.7064\n", "Epoch 316: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 534s 2s/step - loss: 0.2866 - mask_output_loss: 0.1645 - boundary_output_loss: 0.2443 - mask_output_iou_coef: 0.8304 - boundary_output_iou_coef: 0.7064 - val_loss: 0.1981 - val_mask_output_loss: 0.1087 - val_boundary_output_loss: 0.1787 - val_mask_output_iou_coef: 0.8880 - val_boundary_output_iou_coef: 0.7883\n", "Epoch 317/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2831 - mask_output_loss: 0.1611 - boundary_output_loss: 0.2441 - mask_output_iou_coef: 0.8327 - boundary_output_iou_coef: 0.7058\n", "Epoch 317: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 531s 2s/step - loss: 0.2831 - mask_output_loss: 0.1611 - boundary_output_loss: 0.2441 - mask_output_iou_coef: 0.8327 - boundary_output_iou_coef: 0.7058 - val_loss: 0.1951 - val_mask_output_loss: 0.1065 - val_boundary_output_loss: 0.1773 - val_mask_output_iou_coef: 0.8880 - val_boundary_output_iou_coef: 0.7876\n", "Epoch 318/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2923 - mask_output_loss: 0.1691 - boundary_output_loss: 0.2463 - mask_output_iou_coef: 0.8263 - boundary_output_iou_coef: 0.7047\n", "Epoch 318: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 530s 2s/step - loss: 0.2923 - mask_output_loss: 0.1691 - boundary_output_loss: 0.2463 - mask_output_iou_coef: 0.8263 - boundary_output_iou_coef: 0.7047 - val_loss: 0.1960 - val_mask_output_loss: 0.1069 - val_boundary_output_loss: 0.1782 - val_mask_output_iou_coef: 0.8904 - val_boundary_output_iou_coef: 0.7910\n", "Epoch 319/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2928 - mask_output_loss: 0.1676 - boundary_output_loss: 0.2506 - mask_output_iou_coef: 0.8287 - boundary_output_iou_coef: 0.7005\n", "Epoch 319: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 536s 2s/step - loss: 0.2928 - mask_output_loss: 0.1676 - boundary_output_loss: 0.2506 - mask_output_iou_coef: 0.8287 - boundary_output_iou_coef: 0.7005 - val_loss: 0.1984 - val_mask_output_loss: 0.1091 - val_boundary_output_loss: 0.1786 - val_mask_output_iou_coef: 0.8886 - val_boundary_output_iou_coef: 0.7890\n", "Epoch 320/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2798 - mask_output_loss: 0.1584 - boundary_output_loss: 0.2429 - mask_output_iou_coef: 0.8335 - boundary_output_iou_coef: 0.7074\n", "Epoch 320: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 534s 2s/step - loss: 0.2798 - mask_output_loss: 0.1584 - boundary_output_loss: 0.2429 - mask_output_iou_coef: 0.8335 - boundary_output_iou_coef: 0.7074 - val_loss: 0.1920 - val_mask_output_loss: 0.1055 - val_boundary_output_loss: 0.1730 - val_mask_output_iou_coef: 0.8891 - val_boundary_output_iou_coef: 0.7917\n", "Epoch 321/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2831 - mask_output_loss: 0.1599 - boundary_output_loss: 0.2463 - mask_output_iou_coef: 0.8336 - boundary_output_iou_coef: 0.7037\n", "Epoch 321: val_loss did not improve from 0.18864\n", "230/230 [==============================] - 536s 2s/step - loss: 0.2831 - mask_output_loss: 0.1599 - boundary_output_loss: 0.2463 - mask_output_iou_coef: 0.8336 - boundary_output_iou_coef: 0.7037 - val_loss: 0.1897 - val_mask_output_loss: 0.1031 - val_boundary_output_loss: 0.1733 - val_mask_output_iou_coef: 0.8891 - val_boundary_output_iou_coef: 0.7873\n", "Epoch 322/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2930 - mask_output_loss: 0.1699 - boundary_output_loss: 0.2462 - mask_output_iou_coef: 0.8262 - boundary_output_iou_coef: 0.7041\n", "Epoch 322: val_loss improved from 0.18864 to 0.18784, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 534s 2s/step - loss: 0.2930 - mask_output_loss: 0.1699 - boundary_output_loss: 0.2462 - mask_output_iou_coef: 0.8262 - boundary_output_iou_coef: 0.7041 - val_loss: 0.1878 - val_mask_output_loss: 0.1020 - val_boundary_output_loss: 0.1717 - val_mask_output_iou_coef: 0.8929 - val_boundary_output_iou_coef: 0.7913\n", "Epoch 323/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2994 - mask_output_loss: 0.1742 - boundary_output_loss: 0.2504 - mask_output_iou_coef: 0.8219 - boundary_output_iou_coef: 0.7003\n", "Epoch 323: val_loss did not improve from 0.18784\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2994 - mask_output_loss: 0.1742 - boundary_output_loss: 0.2504 - mask_output_iou_coef: 0.8219 - boundary_output_iou_coef: 0.7003 - val_loss: 0.1899 - val_mask_output_loss: 0.1037 - val_boundary_output_loss: 0.1724 - val_mask_output_iou_coef: 0.8915 - val_boundary_output_iou_coef: 0.7900\n", "Epoch 324/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2856 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2469 - mask_output_iou_coef: 0.8319 - boundary_output_iou_coef: 0.7036\n", "Epoch 324: val_loss did not improve from 0.18784\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2856 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2469 - mask_output_iou_coef: 0.8319 - boundary_output_iou_coef: 0.7036 - val_loss: 0.1934 - val_mask_output_loss: 0.1039 - val_boundary_output_loss: 0.1790 - val_mask_output_iou_coef: 0.8906 - val_boundary_output_iou_coef: 0.7872\n", "Epoch 325/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2788 - mask_output_loss: 0.1575 - boundary_output_loss: 0.2426 - mask_output_iou_coef: 0.8370 - boundary_output_iou_coef: 0.7090\n", "Epoch 325: val_loss improved from 0.18784 to 0.18691, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2788 - mask_output_loss: 0.1575 - boundary_output_loss: 0.2426 - mask_output_iou_coef: 0.8370 - boundary_output_iou_coef: 0.7090 - val_loss: 0.1869 - val_mask_output_loss: 0.1016 - val_boundary_output_loss: 0.1707 - val_mask_output_iou_coef: 0.8943 - val_boundary_output_iou_coef: 0.7939\n", "Epoch 326/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2872 - mask_output_loss: 0.1647 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8322 - boundary_output_iou_coef: 0.7065\n", "Epoch 326: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2872 - mask_output_loss: 0.1647 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8322 - boundary_output_iou_coef: 0.7065 - val_loss: 0.1994 - val_mask_output_loss: 0.1106 - val_boundary_output_loss: 0.1777 - val_mask_output_iou_coef: 0.8838 - val_boundary_output_iou_coef: 0.7882\n", "Epoch 327/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2881 - mask_output_loss: 0.1650 - boundary_output_loss: 0.2462 - mask_output_iou_coef: 0.8303 - boundary_output_iou_coef: 0.7039\n", "Epoch 327: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2881 - mask_output_loss: 0.1650 - boundary_output_loss: 0.2462 - mask_output_iou_coef: 0.8303 - boundary_output_iou_coef: 0.7039 - val_loss: 0.1975 - val_mask_output_loss: 0.1081 - val_boundary_output_loss: 0.1788 - val_mask_output_iou_coef: 0.8878 - val_boundary_output_iou_coef: 0.7855\n", "Epoch 328/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2882 - mask_output_loss: 0.1650 - boundary_output_loss: 0.2466 - mask_output_iou_coef: 0.8301 - boundary_output_iou_coef: 0.7039\n", "Epoch 328: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2882 - mask_output_loss: 0.1650 - boundary_output_loss: 0.2466 - mask_output_iou_coef: 0.8301 - boundary_output_iou_coef: 0.7039 - val_loss: 0.1923 - val_mask_output_loss: 0.1048 - val_boundary_output_loss: 0.1752 - val_mask_output_iou_coef: 0.8893 - val_boundary_output_iou_coef: 0.7871\n", "Epoch 329/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2892 - mask_output_loss: 0.1660 - boundary_output_loss: 0.2464 - mask_output_iou_coef: 0.8297 - boundary_output_iou_coef: 0.7039\n", "Epoch 329: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2892 - mask_output_loss: 0.1660 - boundary_output_loss: 0.2464 - mask_output_iou_coef: 0.8297 - boundary_output_iou_coef: 0.7039 - val_loss: 0.1981 - val_mask_output_loss: 0.1084 - val_boundary_output_loss: 0.1793 - val_mask_output_iou_coef: 0.8854 - val_boundary_output_iou_coef: 0.7870\n", "Epoch 330/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2838 - mask_output_loss: 0.1610 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8329 - boundary_output_iou_coef: 0.7053\n", "Epoch 330: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2838 - mask_output_loss: 0.1610 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8329 - boundary_output_iou_coef: 0.7053 - val_loss: 0.1929 - val_mask_output_loss: 0.1056 - val_boundary_output_loss: 0.1746 - val_mask_output_iou_coef: 0.8915 - val_boundary_output_iou_coef: 0.7922\n", "Epoch 331/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2866 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2490 - mask_output_iou_coef: 0.8326 - boundary_output_iou_coef: 0.7020\n", "Epoch 331: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 529s 2s/step - loss: 0.2866 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2490 - mask_output_iou_coef: 0.8326 - boundary_output_iou_coef: 0.7020 - val_loss: 0.2011 - val_mask_output_loss: 0.1105 - val_boundary_output_loss: 0.1813 - val_mask_output_iou_coef: 0.8847 - val_boundary_output_iou_coef: 0.7829\n", "Epoch 332/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2819 - mask_output_loss: 0.1597 - boundary_output_loss: 0.2444 - mask_output_iou_coef: 0.8337 - boundary_output_iou_coef: 0.7052\n", "Epoch 332: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 529s 2s/step - loss: 0.2819 - mask_output_loss: 0.1597 - boundary_output_loss: 0.2444 - mask_output_iou_coef: 0.8337 - boundary_output_iou_coef: 0.7052 - val_loss: 0.1956 - val_mask_output_loss: 0.1067 - val_boundary_output_loss: 0.1778 - val_mask_output_iou_coef: 0.8903 - val_boundary_output_iou_coef: 0.7873\n", "Epoch 333/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2816 - mask_output_loss: 0.1590 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8355 - boundary_output_iou_coef: 0.7052\n", "Epoch 333: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 535s 2s/step - loss: 0.2816 - mask_output_loss: 0.1590 - boundary_output_loss: 0.2451 - mask_output_iou_coef: 0.8355 - boundary_output_iou_coef: 0.7052 - val_loss: 0.1932 - val_mask_output_loss: 0.1051 - val_boundary_output_loss: 0.1762 - val_mask_output_iou_coef: 0.8911 - val_boundary_output_iou_coef: 0.7895\n", "Epoch 334/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2804 - mask_output_loss: 0.1587 - boundary_output_loss: 0.2433 - mask_output_iou_coef: 0.8348 - boundary_output_iou_coef: 0.7065\n", "Epoch 334: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 535s 2s/step - loss: 0.2804 - mask_output_loss: 0.1587 - boundary_output_loss: 0.2433 - mask_output_iou_coef: 0.8348 - boundary_output_iou_coef: 0.7065 - val_loss: 0.1938 - val_mask_output_loss: 0.1066 - val_boundary_output_loss: 0.1744 - val_mask_output_iou_coef: 0.8885 - val_boundary_output_iou_coef: 0.7909\n", "Epoch 335/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2952 - mask_output_loss: 0.1692 - boundary_output_loss: 0.2521 - mask_output_iou_coef: 0.8259 - boundary_output_iou_coef: 0.6988\n", "Epoch 335: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 536s 2s/step - loss: 0.2952 - mask_output_loss: 0.1692 - boundary_output_loss: 0.2521 - mask_output_iou_coef: 0.8259 - boundary_output_iou_coef: 0.6988 - val_loss: 0.1896 - val_mask_output_loss: 0.1028 - val_boundary_output_loss: 0.1736 - val_mask_output_iou_coef: 0.8926 - val_boundary_output_iou_coef: 0.7904\n", "Epoch 336/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2939 - mask_output_loss: 0.1707 - boundary_output_loss: 0.2465 - mask_output_iou_coef: 0.8264 - boundary_output_iou_coef: 0.7045\n", "Epoch 336: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 538s 2s/step - loss: 0.2939 - mask_output_loss: 0.1707 - boundary_output_loss: 0.2465 - mask_output_iou_coef: 0.8264 - boundary_output_iou_coef: 0.7045 - val_loss: 0.1957 - val_mask_output_loss: 0.1070 - val_boundary_output_loss: 0.1774 - val_mask_output_iou_coef: 0.8894 - val_boundary_output_iou_coef: 0.7880\n", "Epoch 337/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2841 - mask_output_loss: 0.1624 - boundary_output_loss: 0.2434 - mask_output_iou_coef: 0.8325 - boundary_output_iou_coef: 0.7076\n", "Epoch 337: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 538s 2s/step - loss: 0.2841 - mask_output_loss: 0.1624 - boundary_output_loss: 0.2434 - mask_output_iou_coef: 0.8325 - boundary_output_iou_coef: 0.7076 - val_loss: 0.1927 - val_mask_output_loss: 0.1059 - val_boundary_output_loss: 0.1736 - val_mask_output_iou_coef: 0.8917 - val_boundary_output_iou_coef: 0.7937\n", "Epoch 338/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2835 - mask_output_loss: 0.1588 - boundary_output_loss: 0.2495 - mask_output_iou_coef: 0.8341 - boundary_output_iou_coef: 0.7018\n", "Epoch 338: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 531s 2s/step - loss: 0.2835 - mask_output_loss: 0.1588 - boundary_output_loss: 0.2495 - mask_output_iou_coef: 0.8341 - boundary_output_iou_coef: 0.7018 - val_loss: 0.1878 - val_mask_output_loss: 0.1017 - val_boundary_output_loss: 0.1722 - val_mask_output_iou_coef: 0.8962 - val_boundary_output_iou_coef: 0.7947\n", "Epoch 339/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2827 - mask_output_loss: 0.1599 - boundary_output_loss: 0.2455 - mask_output_iou_coef: 0.8344 - boundary_output_iou_coef: 0.7041\n", "Epoch 339: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 538s 2s/step - loss: 0.2827 - mask_output_loss: 0.1599 - boundary_output_loss: 0.2455 - mask_output_iou_coef: 0.8344 - boundary_output_iou_coef: 0.7041 - val_loss: 0.1915 - val_mask_output_loss: 0.1035 - val_boundary_output_loss: 0.1760 - val_mask_output_iou_coef: 0.8938 - val_boundary_output_iou_coef: 0.7902\n", "Epoch 340/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2879 - mask_output_loss: 0.1636 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8310 - boundary_output_iou_coef: 0.7026\n", "Epoch 340: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 529s 2s/step - loss: 0.2879 - mask_output_loss: 0.1636 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8310 - boundary_output_iou_coef: 0.7026 - val_loss: 0.2003 - val_mask_output_loss: 0.1110 - val_boundary_output_loss: 0.1786 - val_mask_output_iou_coef: 0.8850 - val_boundary_output_iou_coef: 0.7850\n", "Epoch 341/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2883 - mask_output_loss: 0.1646 - boundary_output_loss: 0.2474 - mask_output_iou_coef: 0.8306 - boundary_output_iou_coef: 0.7032\n", "Epoch 341: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 526s 2s/step - loss: 0.2883 - mask_output_loss: 0.1646 - boundary_output_loss: 0.2474 - mask_output_iou_coef: 0.8306 - boundary_output_iou_coef: 0.7032 - val_loss: 0.1931 - val_mask_output_loss: 0.1045 - val_boundary_output_loss: 0.1773 - val_mask_output_iou_coef: 0.8885 - val_boundary_output_iou_coef: 0.7879\n", "Epoch 342/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2910 - mask_output_loss: 0.1675 - boundary_output_loss: 0.2468 - mask_output_iou_coef: 0.8301 - boundary_output_iou_coef: 0.7044\n", "Epoch 342: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 533s 2s/step - loss: 0.2910 - mask_output_loss: 0.1675 - boundary_output_loss: 0.2468 - mask_output_iou_coef: 0.8301 - boundary_output_iou_coef: 0.7044 - val_loss: 0.1926 - val_mask_output_loss: 0.1051 - val_boundary_output_loss: 0.1750 - val_mask_output_iou_coef: 0.8894 - val_boundary_output_iou_coef: 0.7887\n", "Epoch 343/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2941 - mask_output_loss: 0.1684 - boundary_output_loss: 0.2514 - mask_output_iou_coef: 0.8287 - boundary_output_iou_coef: 0.6989\n", "Epoch 343: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 539s 2s/step - loss: 0.2941 - mask_output_loss: 0.1684 - boundary_output_loss: 0.2514 - mask_output_iou_coef: 0.8287 - boundary_output_iou_coef: 0.6989 - val_loss: 0.1939 - val_mask_output_loss: 0.1053 - val_boundary_output_loss: 0.1772 - val_mask_output_iou_coef: 0.8914 - val_boundary_output_iou_coef: 0.7873\n", "Epoch 344/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2950 - mask_output_loss: 0.1701 - boundary_output_loss: 0.2498 - mask_output_iou_coef: 0.8259 - boundary_output_iou_coef: 0.7002\n", "Epoch 344: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 522s 2s/step - loss: 0.2950 - mask_output_loss: 0.1701 - boundary_output_loss: 0.2498 - mask_output_iou_coef: 0.8259 - boundary_output_iou_coef: 0.7002 - val_loss: 0.1932 - val_mask_output_loss: 0.1048 - val_boundary_output_loss: 0.1768 - val_mask_output_iou_coef: 0.8887 - val_boundary_output_iou_coef: 0.7885\n", "Epoch 345/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2907 - mask_output_loss: 0.1658 - boundary_output_loss: 0.2498 - mask_output_iou_coef: 0.8305 - boundary_output_iou_coef: 0.7008\n", "Epoch 345: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 529s 2s/step - loss: 0.2907 - mask_output_loss: 0.1658 - boundary_output_loss: 0.2498 - mask_output_iou_coef: 0.8305 - boundary_output_iou_coef: 0.7008 - val_loss: 0.1889 - val_mask_output_loss: 0.1025 - val_boundary_output_loss: 0.1729 - val_mask_output_iou_coef: 0.8891 - val_boundary_output_iou_coef: 0.7920\n", "Epoch 346/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2890 - mask_output_loss: 0.1648 - boundary_output_loss: 0.2485 - mask_output_iou_coef: 0.8310 - boundary_output_iou_coef: 0.7019\n", "Epoch 346: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 529s 2s/step - loss: 0.2890 - mask_output_loss: 0.1648 - boundary_output_loss: 0.2485 - mask_output_iou_coef: 0.8310 - boundary_output_iou_coef: 0.7019 - val_loss: 0.1975 - val_mask_output_loss: 0.1083 - val_boundary_output_loss: 0.1783 - val_mask_output_iou_coef: 0.8872 - val_boundary_output_iou_coef: 0.7883\n", "Epoch 347/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2886 - mask_output_loss: 0.1614 - boundary_output_loss: 0.2544 - mask_output_iou_coef: 0.8330 - boundary_output_iou_coef: 0.6961\n", "Epoch 347: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 533s 2s/step - loss: 0.2886 - mask_output_loss: 0.1614 - boundary_output_loss: 0.2544 - mask_output_iou_coef: 0.8330 - boundary_output_iou_coef: 0.6961 - val_loss: 0.1878 - val_mask_output_loss: 0.1024 - val_boundary_output_loss: 0.1707 - val_mask_output_iou_coef: 0.8895 - val_boundary_output_iou_coef: 0.7926\n", "Epoch 348/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2878 - mask_output_loss: 0.1661 - boundary_output_loss: 0.2433 - mask_output_iou_coef: 0.8280 - boundary_output_iou_coef: 0.7072\n", "Epoch 348: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 534s 2s/step - loss: 0.2878 - mask_output_loss: 0.1661 - boundary_output_loss: 0.2433 - mask_output_iou_coef: 0.8280 - boundary_output_iou_coef: 0.7072 - val_loss: 0.1944 - val_mask_output_loss: 0.1069 - val_boundary_output_loss: 0.1750 - val_mask_output_iou_coef: 0.8892 - val_boundary_output_iou_coef: 0.7898\n", "Epoch 349/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2812 - mask_output_loss: 0.1601 - boundary_output_loss: 0.2422 - mask_output_iou_coef: 0.8342 - boundary_output_iou_coef: 0.7084\n", "Epoch 349: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 537s 2s/step - loss: 0.2812 - mask_output_loss: 0.1601 - boundary_output_loss: 0.2422 - mask_output_iou_coef: 0.8342 - boundary_output_iou_coef: 0.7084 - val_loss: 0.1918 - val_mask_output_loss: 0.1048 - val_boundary_output_loss: 0.1741 - val_mask_output_iou_coef: 0.8891 - val_boundary_output_iou_coef: 0.7891\n", "Epoch 350/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2904 - mask_output_loss: 0.1668 - boundary_output_loss: 0.2472 - mask_output_iou_coef: 0.8275 - boundary_output_iou_coef: 0.7036\n", "Epoch 350: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2904 - mask_output_loss: 0.1668 - boundary_output_loss: 0.2472 - mask_output_iou_coef: 0.8275 - boundary_output_iou_coef: 0.7036 - val_loss: 0.1926 - val_mask_output_loss: 0.1053 - val_boundary_output_loss: 0.1745 - val_mask_output_iou_coef: 0.8889 - val_boundary_output_iou_coef: 0.7879\n", "Epoch 351/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2823 - mask_output_loss: 0.1617 - boundary_output_loss: 0.2411 - mask_output_iou_coef: 0.8331 - boundary_output_iou_coef: 0.7089\n", "Epoch 351: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2823 - mask_output_loss: 0.1617 - boundary_output_loss: 0.2411 - mask_output_iou_coef: 0.8331 - boundary_output_iou_coef: 0.7089 - val_loss: 0.1877 - val_mask_output_loss: 0.1019 - val_boundary_output_loss: 0.1716 - val_mask_output_iou_coef: 0.8939 - val_boundary_output_iou_coef: 0.7936\n", "Epoch 352/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2952 - mask_output_loss: 0.1680 - boundary_output_loss: 0.2545 - mask_output_iou_coef: 0.8258 - boundary_output_iou_coef: 0.6960\n", "Epoch 352: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 539s 2s/step - loss: 0.2952 - mask_output_loss: 0.1680 - boundary_output_loss: 0.2545 - mask_output_iou_coef: 0.8258 - boundary_output_iou_coef: 0.6960 - val_loss: 0.2003 - val_mask_output_loss: 0.1093 - val_boundary_output_loss: 0.1821 - val_mask_output_iou_coef: 0.8842 - val_boundary_output_iou_coef: 0.7801\n", "Epoch 353/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2875 - mask_output_loss: 0.1658 - boundary_output_loss: 0.2433 - mask_output_iou_coef: 0.8318 - boundary_output_iou_coef: 0.7075\n", "Epoch 353: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 538s 2s/step - loss: 0.2875 - mask_output_loss: 0.1658 - boundary_output_loss: 0.2433 - mask_output_iou_coef: 0.8318 - boundary_output_iou_coef: 0.7075 - val_loss: 0.1955 - val_mask_output_loss: 0.1072 - val_boundary_output_loss: 0.1766 - val_mask_output_iou_coef: 0.8817 - val_boundary_output_iou_coef: 0.7857\n", "Epoch 354/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2850 - mask_output_loss: 0.1623 - boundary_output_loss: 0.2453 - mask_output_iou_coef: 0.8325 - boundary_output_iou_coef: 0.7060\n", "Epoch 354: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 538s 2s/step - loss: 0.2850 - mask_output_loss: 0.1623 - boundary_output_loss: 0.2453 - mask_output_iou_coef: 0.8325 - boundary_output_iou_coef: 0.7060 - val_loss: 0.1918 - val_mask_output_loss: 0.1050 - val_boundary_output_loss: 0.1737 - val_mask_output_iou_coef: 0.8934 - val_boundary_output_iou_coef: 0.7946\n", "Epoch 355/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2793 - mask_output_loss: 0.1576 - boundary_output_loss: 0.2433 - mask_output_iou_coef: 0.8361 - boundary_output_iou_coef: 0.7072\n", "Epoch 355: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 538s 2s/step - loss: 0.2793 - mask_output_loss: 0.1576 - boundary_output_loss: 0.2433 - mask_output_iou_coef: 0.8361 - boundary_output_iou_coef: 0.7072 - val_loss: 0.1925 - val_mask_output_loss: 0.1038 - val_boundary_output_loss: 0.1775 - val_mask_output_iou_coef: 0.8913 - val_boundary_output_iou_coef: 0.7847\n", "Epoch 356/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2814 - mask_output_loss: 0.1585 - boundary_output_loss: 0.2457 - mask_output_iou_coef: 0.8355 - boundary_output_iou_coef: 0.7045\n", "Epoch 356: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 535s 2s/step - loss: 0.2814 - mask_output_loss: 0.1585 - boundary_output_loss: 0.2457 - mask_output_iou_coef: 0.8355 - boundary_output_iou_coef: 0.7045 - val_loss: 0.1936 - val_mask_output_loss: 0.1047 - val_boundary_output_loss: 0.1779 - val_mask_output_iou_coef: 0.8922 - val_boundary_output_iou_coef: 0.7881\n", "Epoch 357/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2862 - mask_output_loss: 0.1643 - boundary_output_loss: 0.2437 - mask_output_iou_coef: 0.8313 - boundary_output_iou_coef: 0.7072\n", "Epoch 357: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 539s 2s/step - loss: 0.2862 - mask_output_loss: 0.1643 - boundary_output_loss: 0.2437 - mask_output_iou_coef: 0.8313 - boundary_output_iou_coef: 0.7072 - val_loss: 0.1913 - val_mask_output_loss: 0.1041 - val_boundary_output_loss: 0.1744 - val_mask_output_iou_coef: 0.8932 - val_boundary_output_iou_coef: 0.7906\n", "Epoch 358/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2827 - mask_output_loss: 0.1612 - boundary_output_loss: 0.2430 - mask_output_iou_coef: 0.8342 - boundary_output_iou_coef: 0.7077\n", "Epoch 358: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 536s 2s/step - loss: 0.2827 - mask_output_loss: 0.1612 - boundary_output_loss: 0.2430 - mask_output_iou_coef: 0.8342 - boundary_output_iou_coef: 0.7077 - val_loss: 0.1877 - val_mask_output_loss: 0.1015 - val_boundary_output_loss: 0.1723 - val_mask_output_iou_coef: 0.8917 - val_boundary_output_iou_coef: 0.7895\n", "Epoch 359/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2885 - mask_output_loss: 0.1663 - boundary_output_loss: 0.2444 - mask_output_iou_coef: 0.8288 - boundary_output_iou_coef: 0.7060\n", "Epoch 359: val_loss did not improve from 0.18691\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2885 - mask_output_loss: 0.1663 - boundary_output_loss: 0.2444 - mask_output_iou_coef: 0.8288 - boundary_output_iou_coef: 0.7060 - val_loss: 0.1953 - val_mask_output_loss: 0.1072 - val_boundary_output_loss: 0.1762 - val_mask_output_iou_coef: 0.8911 - val_boundary_output_iou_coef: 0.7906\n", "Epoch 360/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2822 - mask_output_loss: 0.1602 - boundary_output_loss: 0.2441 - mask_output_iou_coef: 0.8355 - boundary_output_iou_coef: 0.7073\n", "Epoch 360: val_loss improved from 0.18691 to 0.18584, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2822 - mask_output_loss: 0.1602 - boundary_output_loss: 0.2441 - mask_output_iou_coef: 0.8355 - boundary_output_iou_coef: 0.7073 - val_loss: 0.1858 - val_mask_output_loss: 0.1005 - val_boundary_output_loss: 0.1706 - val_mask_output_iou_coef: 0.8925 - val_boundary_output_iou_coef: 0.7932\n", "Epoch 361/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2792 - mask_output_loss: 0.1577 - boundary_output_loss: 0.2429 - mask_output_iou_coef: 0.8377 - boundary_output_iou_coef: 0.7088\n", "Epoch 361: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2792 - mask_output_loss: 0.1577 - boundary_output_loss: 0.2429 - mask_output_iou_coef: 0.8377 - boundary_output_iou_coef: 0.7088 - val_loss: 0.1920 - val_mask_output_loss: 0.1048 - val_boundary_output_loss: 0.1744 - val_mask_output_iou_coef: 0.8914 - val_boundary_output_iou_coef: 0.7902\n", "Epoch 362/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2739 - mask_output_loss: 0.1528 - boundary_output_loss: 0.2423 - mask_output_iou_coef: 0.8402 - boundary_output_iou_coef: 0.7089\n", "Epoch 362: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2739 - mask_output_loss: 0.1528 - boundary_output_loss: 0.2423 - mask_output_iou_coef: 0.8402 - boundary_output_iou_coef: 0.7089 - val_loss: 0.1914 - val_mask_output_loss: 0.1042 - val_boundary_output_loss: 0.1744 - val_mask_output_iou_coef: 0.8876 - val_boundary_output_iou_coef: 0.7874\n", "Epoch 363/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2817 - mask_output_loss: 0.1581 - boundary_output_loss: 0.2471 - mask_output_iou_coef: 0.8343 - boundary_output_iou_coef: 0.7032\n", "Epoch 363: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2817 - mask_output_loss: 0.1581 - boundary_output_loss: 0.2471 - mask_output_iou_coef: 0.8343 - boundary_output_iou_coef: 0.7032 - val_loss: 0.1957 - val_mask_output_loss: 0.1062 - val_boundary_output_loss: 0.1789 - val_mask_output_iou_coef: 0.8911 - val_boundary_output_iou_coef: 0.7867\n", "Epoch 364/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2948 - mask_output_loss: 0.1683 - boundary_output_loss: 0.2530 - mask_output_iou_coef: 0.8274 - boundary_output_iou_coef: 0.6976\n", "Epoch 364: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 536s 2s/step - loss: 0.2948 - mask_output_loss: 0.1683 - boundary_output_loss: 0.2530 - mask_output_iou_coef: 0.8274 - boundary_output_iou_coef: 0.6976 - val_loss: 0.1931 - val_mask_output_loss: 0.1050 - val_boundary_output_loss: 0.1763 - val_mask_output_iou_coef: 0.8922 - val_boundary_output_iou_coef: 0.7889\n", "Epoch 365/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2837 - mask_output_loss: 0.1620 - boundary_output_loss: 0.2434 - mask_output_iou_coef: 0.8314 - boundary_output_iou_coef: 0.7070\n", "Epoch 365: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 546s 2s/step - loss: 0.2837 - mask_output_loss: 0.1620 - boundary_output_loss: 0.2434 - mask_output_iou_coef: 0.8314 - boundary_output_iou_coef: 0.7070 - val_loss: 0.1950 - val_mask_output_loss: 0.1063 - val_boundary_output_loss: 0.1775 - val_mask_output_iou_coef: 0.8910 - val_boundary_output_iou_coef: 0.7875\n", "Epoch 366/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2926 - mask_output_loss: 0.1683 - boundary_output_loss: 0.2486 - mask_output_iou_coef: 0.8279 - boundary_output_iou_coef: 0.7025\n", "Epoch 366: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 526s 2s/step - loss: 0.2926 - mask_output_loss: 0.1683 - boundary_output_loss: 0.2486 - mask_output_iou_coef: 0.8279 - boundary_output_iou_coef: 0.7025 - val_loss: 0.1919 - val_mask_output_loss: 0.1045 - val_boundary_output_loss: 0.1748 - val_mask_output_iou_coef: 0.8887 - val_boundary_output_iou_coef: 0.7886\n", "Epoch 367/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2823 - mask_output_loss: 0.1606 - boundary_output_loss: 0.2434 - mask_output_iou_coef: 0.8331 - boundary_output_iou_coef: 0.7076\n", "Epoch 367: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 512s 2s/step - loss: 0.2823 - mask_output_loss: 0.1606 - boundary_output_loss: 0.2434 - mask_output_iou_coef: 0.8331 - boundary_output_iou_coef: 0.7076 - val_loss: 0.1947 - val_mask_output_loss: 0.1058 - val_boundary_output_loss: 0.1778 - val_mask_output_iou_coef: 0.8876 - val_boundary_output_iou_coef: 0.7843\n", "Epoch 368/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2816 - mask_output_loss: 0.1600 - boundary_output_loss: 0.2431 - mask_output_iou_coef: 0.8337 - boundary_output_iou_coef: 0.7073\n", "Epoch 368: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 530s 2s/step - loss: 0.2816 - mask_output_loss: 0.1600 - boundary_output_loss: 0.2431 - mask_output_iou_coef: 0.8337 - boundary_output_iou_coef: 0.7073 - val_loss: 0.1885 - val_mask_output_loss: 0.1023 - val_boundary_output_loss: 0.1724 - val_mask_output_iou_coef: 0.8941 - val_boundary_output_iou_coef: 0.7918\n", "Epoch 369/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2856 - mask_output_loss: 0.1633 - boundary_output_loss: 0.2447 - mask_output_iou_coef: 0.8321 - boundary_output_iou_coef: 0.7060\n", "Epoch 369: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 528s 2s/step - loss: 0.2856 - mask_output_loss: 0.1633 - boundary_output_loss: 0.2447 - mask_output_iou_coef: 0.8321 - boundary_output_iou_coef: 0.7060 - val_loss: 0.1871 - val_mask_output_loss: 0.1016 - val_boundary_output_loss: 0.1711 - val_mask_output_iou_coef: 0.8927 - val_boundary_output_iou_coef: 0.7920\n", "Epoch 370/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2838 - mask_output_loss: 0.1608 - boundary_output_loss: 0.2458 - mask_output_iou_coef: 0.8334 - boundary_output_iou_coef: 0.7043\n", "Epoch 370: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 533s 2s/step - loss: 0.2838 - mask_output_loss: 0.1608 - boundary_output_loss: 0.2458 - mask_output_iou_coef: 0.8334 - boundary_output_iou_coef: 0.7043 - val_loss: 0.1901 - val_mask_output_loss: 0.1034 - val_boundary_output_loss: 0.1735 - val_mask_output_iou_coef: 0.8936 - val_boundary_output_iou_coef: 0.7911\n", "Epoch 371/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2766 - mask_output_loss: 0.1529 - boundary_output_loss: 0.2473 - mask_output_iou_coef: 0.8404 - boundary_output_iou_coef: 0.7048\n", "Epoch 371: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 532s 2s/step - loss: 0.2766 - mask_output_loss: 0.1529 - boundary_output_loss: 0.2473 - mask_output_iou_coef: 0.8404 - boundary_output_iou_coef: 0.7048 - val_loss: 0.1896 - val_mask_output_loss: 0.1035 - val_boundary_output_loss: 0.1722 - val_mask_output_iou_coef: 0.8944 - val_boundary_output_iou_coef: 0.7917\n", "Epoch 372/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2905 - mask_output_loss: 0.1675 - boundary_output_loss: 0.2461 - mask_output_iou_coef: 0.8289 - boundary_output_iou_coef: 0.7056\n", "Epoch 372: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 534s 2s/step - loss: 0.2905 - mask_output_loss: 0.1675 - boundary_output_loss: 0.2461 - mask_output_iou_coef: 0.8289 - boundary_output_iou_coef: 0.7056 - val_loss: 0.1948 - val_mask_output_loss: 0.1059 - val_boundary_output_loss: 0.1778 - val_mask_output_iou_coef: 0.8922 - val_boundary_output_iou_coef: 0.7889\n", "Epoch 373/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2808 - mask_output_loss: 0.1589 - boundary_output_loss: 0.2438 - mask_output_iou_coef: 0.8357 - boundary_output_iou_coef: 0.7069\n", "Epoch 373: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 535s 2s/step - loss: 0.2808 - mask_output_loss: 0.1589 - boundary_output_loss: 0.2438 - mask_output_iou_coef: 0.8357 - boundary_output_iou_coef: 0.7069 - val_loss: 0.1875 - val_mask_output_loss: 0.1024 - val_boundary_output_loss: 0.1704 - val_mask_output_iou_coef: 0.8909 - val_boundary_output_iou_coef: 0.7907\n", "Epoch 374/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2876 - mask_output_loss: 0.1669 - boundary_output_loss: 0.2416 - mask_output_iou_coef: 0.8293 - boundary_output_iou_coef: 0.7095\n", "Epoch 374: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 535s 2s/step - loss: 0.2876 - mask_output_loss: 0.1669 - boundary_output_loss: 0.2416 - mask_output_iou_coef: 0.8293 - boundary_output_iou_coef: 0.7095 - val_loss: 0.1941 - val_mask_output_loss: 0.1065 - val_boundary_output_loss: 0.1753 - val_mask_output_iou_coef: 0.8903 - val_boundary_output_iou_coef: 0.7888\n", "Epoch 375/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2899 - mask_output_loss: 0.1669 - boundary_output_loss: 0.2459 - mask_output_iou_coef: 0.8302 - boundary_output_iou_coef: 0.7054\n", "Epoch 375: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 534s 2s/step - loss: 0.2899 - mask_output_loss: 0.1669 - boundary_output_loss: 0.2459 - mask_output_iou_coef: 0.8302 - boundary_output_iou_coef: 0.7054 - val_loss: 0.1882 - val_mask_output_loss: 0.1018 - val_boundary_output_loss: 0.1729 - val_mask_output_iou_coef: 0.8960 - val_boundary_output_iou_coef: 0.7930\n", "Epoch 376/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2737 - mask_output_loss: 0.1522 - boundary_output_loss: 0.2430 - mask_output_iou_coef: 0.8401 - boundary_output_iou_coef: 0.7073\n", "Epoch 376: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 533s 2s/step - loss: 0.2737 - mask_output_loss: 0.1522 - boundary_output_loss: 0.2430 - mask_output_iou_coef: 0.8401 - boundary_output_iou_coef: 0.7073 - val_loss: 0.1882 - val_mask_output_loss: 0.1020 - val_boundary_output_loss: 0.1723 - val_mask_output_iou_coef: 0.8931 - val_boundary_output_iou_coef: 0.7919\n", "Epoch 377/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2780 - mask_output_loss: 0.1563 - boundary_output_loss: 0.2435 - mask_output_iou_coef: 0.8370 - boundary_output_iou_coef: 0.7068\n", "Epoch 377: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 531s 2s/step - loss: 0.2780 - mask_output_loss: 0.1563 - boundary_output_loss: 0.2435 - mask_output_iou_coef: 0.8370 - boundary_output_iou_coef: 0.7068 - val_loss: 0.1949 - val_mask_output_loss: 0.1061 - val_boundary_output_loss: 0.1776 - val_mask_output_iou_coef: 0.8901 - val_boundary_output_iou_coef: 0.7880\n", "Epoch 378/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2872 - mask_output_loss: 0.1646 - boundary_output_loss: 0.2453 - mask_output_iou_coef: 0.8298 - boundary_output_iou_coef: 0.7060\n", "Epoch 378: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2872 - mask_output_loss: 0.1646 - boundary_output_loss: 0.2453 - mask_output_iou_coef: 0.8298 - boundary_output_iou_coef: 0.7060 - val_loss: 0.1924 - val_mask_output_loss: 0.1055 - val_boundary_output_loss: 0.1738 - val_mask_output_iou_coef: 0.8891 - val_boundary_output_iou_coef: 0.7912\n", "Epoch 379/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2765 - mask_output_loss: 0.1547 - boundary_output_loss: 0.2436 - mask_output_iou_coef: 0.8373 - boundary_output_iou_coef: 0.7062\n", "Epoch 379: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 537s 2s/step - loss: 0.2765 - mask_output_loss: 0.1547 - boundary_output_loss: 0.2436 - mask_output_iou_coef: 0.8373 - boundary_output_iou_coef: 0.7062 - val_loss: 0.1925 - val_mask_output_loss: 0.1047 - val_boundary_output_loss: 0.1757 - val_mask_output_iou_coef: 0.8921 - val_boundary_output_iou_coef: 0.7899\n", "Epoch 380/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2889 - mask_output_loss: 0.1663 - boundary_output_loss: 0.2452 - mask_output_iou_coef: 0.8291 - boundary_output_iou_coef: 0.7051\n", "Epoch 380: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 537s 2s/step - loss: 0.2889 - mask_output_loss: 0.1663 - boundary_output_loss: 0.2452 - mask_output_iou_coef: 0.8291 - boundary_output_iou_coef: 0.7051 - val_loss: 0.1900 - val_mask_output_loss: 0.1031 - val_boundary_output_loss: 0.1739 - val_mask_output_iou_coef: 0.8914 - val_boundary_output_iou_coef: 0.7918\n", "Epoch 381/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2866 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2472 - mask_output_iou_coef: 0.8329 - boundary_output_iou_coef: 0.7040\n", "Epoch 381: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2866 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2472 - mask_output_iou_coef: 0.8329 - boundary_output_iou_coef: 0.7040 - val_loss: 0.1910 - val_mask_output_loss: 0.1044 - val_boundary_output_loss: 0.1732 - val_mask_output_iou_coef: 0.8900 - val_boundary_output_iou_coef: 0.7906\n", "Epoch 382/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2837 - mask_output_loss: 0.1619 - boundary_output_loss: 0.2436 - mask_output_iou_coef: 0.8336 - boundary_output_iou_coef: 0.7076\n", "Epoch 382: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 539s 2s/step - loss: 0.2837 - mask_output_loss: 0.1619 - boundary_output_loss: 0.2436 - mask_output_iou_coef: 0.8336 - boundary_output_iou_coef: 0.7076 - val_loss: 0.1978 - val_mask_output_loss: 0.1084 - val_boundary_output_loss: 0.1787 - val_mask_output_iou_coef: 0.8926 - val_boundary_output_iou_coef: 0.7886\n", "Epoch 383/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2800 - mask_output_loss: 0.1595 - boundary_output_loss: 0.2409 - mask_output_iou_coef: 0.8354 - boundary_output_iou_coef: 0.7102\n", "Epoch 383: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2800 - mask_output_loss: 0.1595 - boundary_output_loss: 0.2409 - mask_output_iou_coef: 0.8354 - boundary_output_iou_coef: 0.7102 - val_loss: 0.1926 - val_mask_output_loss: 0.1053 - val_boundary_output_loss: 0.1747 - val_mask_output_iou_coef: 0.8893 - val_boundary_output_iou_coef: 0.7876\n", "Epoch 384/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2835 - mask_output_loss: 0.1635 - boundary_output_loss: 0.2401 - mask_output_iou_coef: 0.8305 - boundary_output_iou_coef: 0.7111\n", "Epoch 384: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2835 - mask_output_loss: 0.1635 - boundary_output_loss: 0.2401 - mask_output_iou_coef: 0.8305 - boundary_output_iou_coef: 0.7111 - val_loss: 0.1927 - val_mask_output_loss: 0.1057 - val_boundary_output_loss: 0.1742 - val_mask_output_iou_coef: 0.8924 - val_boundary_output_iou_coef: 0.7910\n", "Epoch 385/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2926 - mask_output_loss: 0.1684 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8305 - boundary_output_iou_coef: 0.7026\n", "Epoch 385: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2926 - mask_output_loss: 0.1684 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8305 - boundary_output_iou_coef: 0.7026 - val_loss: 0.1884 - val_mask_output_loss: 0.1022 - val_boundary_output_loss: 0.1724 - val_mask_output_iou_coef: 0.8937 - val_boundary_output_iou_coef: 0.7906\n", "Epoch 386/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2913 - mask_output_loss: 0.1675 - boundary_output_loss: 0.2474 - mask_output_iou_coef: 0.8289 - boundary_output_iou_coef: 0.7045\n", "Epoch 386: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2913 - mask_output_loss: 0.1675 - boundary_output_loss: 0.2474 - mask_output_iou_coef: 0.8289 - boundary_output_iou_coef: 0.7045 - val_loss: 0.1935 - val_mask_output_loss: 0.1053 - val_boundary_output_loss: 0.1764 - val_mask_output_iou_coef: 0.8904 - val_boundary_output_iou_coef: 0.7881\n", "Epoch 387/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2786 - mask_output_loss: 0.1574 - boundary_output_loss: 0.2424 - mask_output_iou_coef: 0.8360 - boundary_output_iou_coef: 0.7085\n", "Epoch 387: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2786 - mask_output_loss: 0.1574 - boundary_output_loss: 0.2424 - mask_output_iou_coef: 0.8360 - boundary_output_iou_coef: 0.7085 - val_loss: 0.1897 - val_mask_output_loss: 0.1027 - val_boundary_output_loss: 0.1741 - val_mask_output_iou_coef: 0.8954 - val_boundary_output_iou_coef: 0.7924\n", "Epoch 388/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2760 - mask_output_loss: 0.1556 - boundary_output_loss: 0.2407 - mask_output_iou_coef: 0.8381 - boundary_output_iou_coef: 0.7103\n", "Epoch 388: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2760 - mask_output_loss: 0.1556 - boundary_output_loss: 0.2407 - mask_output_iou_coef: 0.8381 - boundary_output_iou_coef: 0.7103 - val_loss: 0.1895 - val_mask_output_loss: 0.1028 - val_boundary_output_loss: 0.1734 - val_mask_output_iou_coef: 0.8920 - val_boundary_output_iou_coef: 0.7922\n", "Epoch 389/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2817 - mask_output_loss: 0.1597 - boundary_output_loss: 0.2440 - mask_output_iou_coef: 0.8338 - boundary_output_iou_coef: 0.7074\n", "Epoch 389: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2817 - mask_output_loss: 0.1597 - boundary_output_loss: 0.2440 - mask_output_iou_coef: 0.8338 - boundary_output_iou_coef: 0.7074 - val_loss: 0.1871 - val_mask_output_loss: 0.1011 - val_boundary_output_loss: 0.1721 - val_mask_output_iou_coef: 0.8945 - val_boundary_output_iou_coef: 0.7913\n", "Epoch 390/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2903 - mask_output_loss: 0.1665 - boundary_output_loss: 0.2476 - mask_output_iou_coef: 0.8289 - boundary_output_iou_coef: 0.7022\n", "Epoch 390: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2903 - mask_output_loss: 0.1665 - boundary_output_loss: 0.2476 - mask_output_iou_coef: 0.8289 - boundary_output_iou_coef: 0.7022 - val_loss: 0.1918 - val_mask_output_loss: 0.1041 - val_boundary_output_loss: 0.1755 - val_mask_output_iou_coef: 0.8901 - val_boundary_output_iou_coef: 0.7863\n", "Epoch 391/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2837 - mask_output_loss: 0.1605 - boundary_output_loss: 0.2465 - mask_output_iou_coef: 0.8346 - boundary_output_iou_coef: 0.7050\n", "Epoch 391: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2837 - mask_output_loss: 0.1605 - boundary_output_loss: 0.2465 - mask_output_iou_coef: 0.8346 - boundary_output_iou_coef: 0.7050 - val_loss: 0.1917 - val_mask_output_loss: 0.1030 - val_boundary_output_loss: 0.1774 - val_mask_output_iou_coef: 0.8938 - val_boundary_output_iou_coef: 0.7882\n", "Epoch 392/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2920 - mask_output_loss: 0.1671 - boundary_output_loss: 0.2499 - mask_output_iou_coef: 0.8282 - boundary_output_iou_coef: 0.7006\n", "Epoch 392: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2920 - mask_output_loss: 0.1671 - boundary_output_loss: 0.2499 - mask_output_iou_coef: 0.8282 - boundary_output_iou_coef: 0.7006 - val_loss: 0.1933 - val_mask_output_loss: 0.1046 - val_boundary_output_loss: 0.1775 - val_mask_output_iou_coef: 0.8910 - val_boundary_output_iou_coef: 0.7881\n", "Epoch 393/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2959 - mask_output_loss: 0.1689 - boundary_output_loss: 0.2541 - mask_output_iou_coef: 0.8258 - boundary_output_iou_coef: 0.6971\n", "Epoch 393: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2959 - mask_output_loss: 0.1689 - boundary_output_loss: 0.2541 - mask_output_iou_coef: 0.8258 - boundary_output_iou_coef: 0.6971 - val_loss: 0.1936 - val_mask_output_loss: 0.1052 - val_boundary_output_loss: 0.1767 - val_mask_output_iou_coef: 0.8923 - val_boundary_output_iou_coef: 0.7869\n", "Epoch 394/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2924 - mask_output_loss: 0.1683 - boundary_output_loss: 0.2483 - mask_output_iou_coef: 0.8271 - boundary_output_iou_coef: 0.7022\n", "Epoch 394: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 540s 2s/step - loss: 0.2924 - mask_output_loss: 0.1683 - boundary_output_loss: 0.2483 - mask_output_iou_coef: 0.8271 - boundary_output_iou_coef: 0.7022 - val_loss: 0.1883 - val_mask_output_loss: 0.1020 - val_boundary_output_loss: 0.1726 - val_mask_output_iou_coef: 0.8933 - val_boundary_output_iou_coef: 0.7910\n", "Epoch 395/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2880 - mask_output_loss: 0.1656 - boundary_output_loss: 0.2447 - mask_output_iou_coef: 0.8296 - boundary_output_iou_coef: 0.7054\n", "Epoch 395: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2880 - mask_output_loss: 0.1656 - boundary_output_loss: 0.2447 - mask_output_iou_coef: 0.8296 - boundary_output_iou_coef: 0.7054 - val_loss: 0.1887 - val_mask_output_loss: 0.1024 - val_boundary_output_loss: 0.1726 - val_mask_output_iou_coef: 0.8947 - val_boundary_output_iou_coef: 0.7912\n", "Epoch 396/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2827 - mask_output_loss: 0.1609 - boundary_output_loss: 0.2436 - mask_output_iou_coef: 0.8335 - boundary_output_iou_coef: 0.7071\n", "Epoch 396: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2827 - mask_output_loss: 0.1609 - boundary_output_loss: 0.2436 - mask_output_iou_coef: 0.8335 - boundary_output_iou_coef: 0.7071 - val_loss: 0.1917 - val_mask_output_loss: 0.1033 - val_boundary_output_loss: 0.1769 - val_mask_output_iou_coef: 0.8933 - val_boundary_output_iou_coef: 0.7882\n", "Epoch 397/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2788 - mask_output_loss: 0.1561 - boundary_output_loss: 0.2454 - mask_output_iou_coef: 0.8366 - boundary_output_iou_coef: 0.7050\n", "Epoch 397: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2788 - mask_output_loss: 0.1561 - boundary_output_loss: 0.2454 - mask_output_iou_coef: 0.8366 - boundary_output_iou_coef: 0.7050 - val_loss: 0.1922 - val_mask_output_loss: 0.1034 - val_boundary_output_loss: 0.1776 - val_mask_output_iou_coef: 0.8969 - val_boundary_output_iou_coef: 0.7932\n", "Epoch 398/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2868 - mask_output_loss: 0.1641 - boundary_output_loss: 0.2454 - mask_output_iou_coef: 0.8313 - boundary_output_iou_coef: 0.7052\n", "Epoch 398: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 539s 2s/step - loss: 0.2868 - mask_output_loss: 0.1641 - boundary_output_loss: 0.2454 - mask_output_iou_coef: 0.8313 - boundary_output_iou_coef: 0.7052 - val_loss: 0.1892 - val_mask_output_loss: 0.1031 - val_boundary_output_loss: 0.1722 - val_mask_output_iou_coef: 0.8937 - val_boundary_output_iou_coef: 0.7912\n", "Epoch 399/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2854 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2448 - mask_output_iou_coef: 0.8333 - boundary_output_iou_coef: 0.7064\n", "Epoch 399: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2854 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2448 - mask_output_iou_coef: 0.8333 - boundary_output_iou_coef: 0.7064 - val_loss: 0.1927 - val_mask_output_loss: 0.1050 - val_boundary_output_loss: 0.1753 - val_mask_output_iou_coef: 0.8905 - val_boundary_output_iou_coef: 0.7931\n", "Epoch 400/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2844 - mask_output_loss: 0.1635 - boundary_output_loss: 0.2419 - mask_output_iou_coef: 0.8315 - boundary_output_iou_coef: 0.7091\n", "Epoch 400: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2844 - mask_output_loss: 0.1635 - boundary_output_loss: 0.2419 - mask_output_iou_coef: 0.8315 - boundary_output_iou_coef: 0.7091 - val_loss: 0.1946 - val_mask_output_loss: 0.1069 - val_boundary_output_loss: 0.1754 - val_mask_output_iou_coef: 0.8927 - val_boundary_output_iou_coef: 0.7921\n", "Epoch 401/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2772 - mask_output_loss: 0.1575 - boundary_output_loss: 0.2395 - mask_output_iou_coef: 0.8362 - boundary_output_iou_coef: 0.7110\n", "Epoch 401: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 545s 2s/step - loss: 0.2772 - mask_output_loss: 0.1575 - boundary_output_loss: 0.2395 - mask_output_iou_coef: 0.8362 - boundary_output_iou_coef: 0.7110 - val_loss: 0.1920 - val_mask_output_loss: 0.1046 - val_boundary_output_loss: 0.1748 - val_mask_output_iou_coef: 0.8945 - val_boundary_output_iou_coef: 0.7921\n", "Epoch 402/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2804 - mask_output_loss: 0.1578 - boundary_output_loss: 0.2453 - mask_output_iou_coef: 0.8367 - boundary_output_iou_coef: 0.7052\n", "Epoch 402: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 543s 2s/step - loss: 0.2804 - mask_output_loss: 0.1578 - boundary_output_loss: 0.2453 - mask_output_iou_coef: 0.8367 - boundary_output_iou_coef: 0.7052 - val_loss: 0.1912 - val_mask_output_loss: 0.1040 - val_boundary_output_loss: 0.1744 - val_mask_output_iou_coef: 0.8914 - val_boundary_output_iou_coef: 0.7900\n", "Epoch 403/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2859 - mask_output_loss: 0.1635 - boundary_output_loss: 0.2448 - mask_output_iou_coef: 0.8313 - boundary_output_iou_coef: 0.7062\n", "Epoch 403: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2859 - mask_output_loss: 0.1635 - boundary_output_loss: 0.2448 - mask_output_iou_coef: 0.8313 - boundary_output_iou_coef: 0.7062 - val_loss: 0.1913 - val_mask_output_loss: 0.1045 - val_boundary_output_loss: 0.1736 - val_mask_output_iou_coef: 0.8922 - val_boundary_output_iou_coef: 0.7900\n", "Epoch 404/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2842 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2442 - mask_output_iou_coef: 0.8337 - boundary_output_iou_coef: 0.7070\n", "Epoch 404: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 534s 2s/step - loss: 0.2842 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2442 - mask_output_iou_coef: 0.8337 - boundary_output_iou_coef: 0.7070 - val_loss: 0.1956 - val_mask_output_loss: 0.1069 - val_boundary_output_loss: 0.1774 - val_mask_output_iou_coef: 0.8882 - val_boundary_output_iou_coef: 0.7877\n", "Epoch 405/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2813 - mask_output_loss: 0.1597 - boundary_output_loss: 0.2432 - mask_output_iou_coef: 0.8358 - boundary_output_iou_coef: 0.7068\n", "Epoch 405: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 535s 2s/step - loss: 0.2813 - mask_output_loss: 0.1597 - boundary_output_loss: 0.2432 - mask_output_iou_coef: 0.8358 - boundary_output_iou_coef: 0.7068 - val_loss: 0.1888 - val_mask_output_loss: 0.1035 - val_boundary_output_loss: 0.1707 - val_mask_output_iou_coef: 0.8943 - val_boundary_output_iou_coef: 0.7970\n", "Epoch 406/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2718 - mask_output_loss: 0.1504 - boundary_output_loss: 0.2428 - mask_output_iou_coef: 0.8430 - boundary_output_iou_coef: 0.7082\n", "Epoch 406: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 535s 2s/step - loss: 0.2718 - mask_output_loss: 0.1504 - boundary_output_loss: 0.2428 - mask_output_iou_coef: 0.8430 - boundary_output_iou_coef: 0.7082 - val_loss: 0.1921 - val_mask_output_loss: 0.1045 - val_boundary_output_loss: 0.1751 - val_mask_output_iou_coef: 0.8917 - val_boundary_output_iou_coef: 0.7908\n", "Epoch 407/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2798 - mask_output_loss: 0.1587 - boundary_output_loss: 0.2422 - mask_output_iou_coef: 0.8359 - boundary_output_iou_coef: 0.7092\n", "Epoch 407: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2798 - mask_output_loss: 0.1587 - boundary_output_loss: 0.2422 - mask_output_iou_coef: 0.8359 - boundary_output_iou_coef: 0.7092 - val_loss: 0.1926 - val_mask_output_loss: 0.1052 - val_boundary_output_loss: 0.1748 - val_mask_output_iou_coef: 0.8918 - val_boundary_output_iou_coef: 0.7890\n", "Epoch 408/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2843 - mask_output_loss: 0.1610 - boundary_output_loss: 0.2467 - mask_output_iou_coef: 0.8326 - boundary_output_iou_coef: 0.7048\n", "Epoch 408: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 546s 2s/step - loss: 0.2843 - mask_output_loss: 0.1610 - boundary_output_loss: 0.2467 - mask_output_iou_coef: 0.8326 - boundary_output_iou_coef: 0.7048 - val_loss: 0.1877 - val_mask_output_loss: 0.1014 - val_boundary_output_loss: 0.1725 - val_mask_output_iou_coef: 0.8948 - val_boundary_output_iou_coef: 0.7935\n", "Epoch 409/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2811 - mask_output_loss: 0.1593 - boundary_output_loss: 0.2435 - mask_output_iou_coef: 0.8356 - boundary_output_iou_coef: 0.7072\n", "Epoch 409: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2811 - mask_output_loss: 0.1593 - boundary_output_loss: 0.2435 - mask_output_iou_coef: 0.8356 - boundary_output_iou_coef: 0.7072 - val_loss: 0.1933 - val_mask_output_loss: 0.1058 - val_boundary_output_loss: 0.1749 - val_mask_output_iou_coef: 0.8906 - val_boundary_output_iou_coef: 0.7902\n", "Epoch 410/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2884 - mask_output_loss: 0.1646 - boundary_output_loss: 0.2476 - mask_output_iou_coef: 0.8314 - boundary_output_iou_coef: 0.7025\n", "Epoch 410: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2884 - mask_output_loss: 0.1646 - boundary_output_loss: 0.2476 - mask_output_iou_coef: 0.8314 - boundary_output_iou_coef: 0.7025 - val_loss: 0.1919 - val_mask_output_loss: 0.1051 - val_boundary_output_loss: 0.1737 - val_mask_output_iou_coef: 0.8927 - val_boundary_output_iou_coef: 0.7932\n", "Epoch 411/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2807 - mask_output_loss: 0.1601 - boundary_output_loss: 0.2414 - mask_output_iou_coef: 0.8351 - boundary_output_iou_coef: 0.7105\n", "Epoch 411: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 549s 2s/step - loss: 0.2807 - mask_output_loss: 0.1601 - boundary_output_loss: 0.2414 - mask_output_iou_coef: 0.8351 - boundary_output_iou_coef: 0.7105 - val_loss: 0.1933 - val_mask_output_loss: 0.1055 - val_boundary_output_loss: 0.1757 - val_mask_output_iou_coef: 0.8890 - val_boundary_output_iou_coef: 0.7869\n", "Epoch 412/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2753 - mask_output_loss: 0.1544 - boundary_output_loss: 0.2419 - mask_output_iou_coef: 0.8387 - boundary_output_iou_coef: 0.7093\n", "Epoch 412: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 534s 2s/step - loss: 0.2753 - mask_output_loss: 0.1544 - boundary_output_loss: 0.2419 - mask_output_iou_coef: 0.8387 - boundary_output_iou_coef: 0.7093 - val_loss: 0.1944 - val_mask_output_loss: 0.1060 - val_boundary_output_loss: 0.1769 - val_mask_output_iou_coef: 0.8916 - val_boundary_output_iou_coef: 0.7873\n", "Epoch 413/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2862 - mask_output_loss: 0.1635 - boundary_output_loss: 0.2454 - mask_output_iou_coef: 0.8308 - boundary_output_iou_coef: 0.7052\n", "Epoch 413: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2862 - mask_output_loss: 0.1635 - boundary_output_loss: 0.2454 - mask_output_iou_coef: 0.8308 - boundary_output_iou_coef: 0.7052 - val_loss: 0.1907 - val_mask_output_loss: 0.1032 - val_boundary_output_loss: 0.1749 - val_mask_output_iou_coef: 0.8901 - val_boundary_output_iou_coef: 0.7910\n", "Epoch 414/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2936 - mask_output_loss: 0.1697 - boundary_output_loss: 0.2478 - mask_output_iou_coef: 0.8288 - boundary_output_iou_coef: 0.7034\n", "Epoch 414: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 546s 2s/step - loss: 0.2936 - mask_output_loss: 0.1697 - boundary_output_loss: 0.2478 - mask_output_iou_coef: 0.8288 - boundary_output_iou_coef: 0.7034 - val_loss: 0.1953 - val_mask_output_loss: 0.1064 - val_boundary_output_loss: 0.1777 - val_mask_output_iou_coef: 0.8850 - val_boundary_output_iou_coef: 0.7848\n", "Epoch 415/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2887 - mask_output_loss: 0.1663 - boundary_output_loss: 0.2447 - mask_output_iou_coef: 0.8291 - boundary_output_iou_coef: 0.7057\n", "Epoch 415: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 546s 2s/step - loss: 0.2887 - mask_output_loss: 0.1663 - boundary_output_loss: 0.2447 - mask_output_iou_coef: 0.8291 - boundary_output_iou_coef: 0.7057 - val_loss: 0.1930 - val_mask_output_loss: 0.1051 - val_boundary_output_loss: 0.1758 - val_mask_output_iou_coef: 0.8889 - val_boundary_output_iou_coef: 0.7882\n", "Epoch 416/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2778 - mask_output_loss: 0.1568 - boundary_output_loss: 0.2419 - mask_output_iou_coef: 0.8368 - boundary_output_iou_coef: 0.7089\n", "Epoch 416: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2778 - mask_output_loss: 0.1568 - boundary_output_loss: 0.2419 - mask_output_iou_coef: 0.8368 - boundary_output_iou_coef: 0.7089 - val_loss: 0.1913 - val_mask_output_loss: 0.1052 - val_boundary_output_loss: 0.1721 - val_mask_output_iou_coef: 0.8919 - val_boundary_output_iou_coef: 0.7914\n", "Epoch 417/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2730 - mask_output_loss: 0.1537 - boundary_output_loss: 0.2387 - mask_output_iou_coef: 0.8391 - boundary_output_iou_coef: 0.7121\n", "Epoch 417: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2730 - mask_output_loss: 0.1537 - boundary_output_loss: 0.2387 - mask_output_iou_coef: 0.8391 - boundary_output_iou_coef: 0.7121 - val_loss: 0.1879 - val_mask_output_loss: 0.1019 - val_boundary_output_loss: 0.1720 - val_mask_output_iou_coef: 0.8922 - val_boundary_output_iou_coef: 0.7932\n", "Epoch 418/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2796 - mask_output_loss: 0.1579 - boundary_output_loss: 0.2435 - mask_output_iou_coef: 0.8361 - boundary_output_iou_coef: 0.7085\n", "Epoch 418: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 546s 2s/step - loss: 0.2796 - mask_output_loss: 0.1579 - boundary_output_loss: 0.2435 - mask_output_iou_coef: 0.8361 - boundary_output_iou_coef: 0.7085 - val_loss: 0.1939 - val_mask_output_loss: 0.1054 - val_boundary_output_loss: 0.1770 - val_mask_output_iou_coef: 0.8922 - val_boundary_output_iou_coef: 0.7878\n", "Epoch 419/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2849 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2437 - mask_output_iou_coef: 0.8314 - boundary_output_iou_coef: 0.7065\n", "Epoch 419: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 545s 2s/step - loss: 0.2849 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2437 - mask_output_iou_coef: 0.8314 - boundary_output_iou_coef: 0.7065 - val_loss: 0.1900 - val_mask_output_loss: 0.1037 - val_boundary_output_loss: 0.1727 - val_mask_output_iou_coef: 0.8937 - val_boundary_output_iou_coef: 0.7925\n", "Epoch 420/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2788 - mask_output_loss: 0.1595 - boundary_output_loss: 0.2385 - mask_output_iou_coef: 0.8348 - boundary_output_iou_coef: 0.7120\n", "Epoch 420: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 543s 2s/step - loss: 0.2788 - mask_output_loss: 0.1595 - boundary_output_loss: 0.2385 - mask_output_iou_coef: 0.8348 - boundary_output_iou_coef: 0.7120 - val_loss: 0.1897 - val_mask_output_loss: 0.1026 - val_boundary_output_loss: 0.1742 - val_mask_output_iou_coef: 0.8944 - val_boundary_output_iou_coef: 0.7906\n", "Epoch 421/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2771 - mask_output_loss: 0.1557 - boundary_output_loss: 0.2428 - mask_output_iou_coef: 0.8380 - boundary_output_iou_coef: 0.7086\n", "Epoch 421: val_loss did not improve from 0.18584\n", "230/230 [==============================] - 545s 2s/step - loss: 0.2771 - mask_output_loss: 0.1557 - boundary_output_loss: 0.2428 - mask_output_iou_coef: 0.8380 - boundary_output_iou_coef: 0.7086 - val_loss: 0.1872 - val_mask_output_loss: 0.1007 - val_boundary_output_loss: 0.1730 - val_mask_output_iou_coef: 0.8971 - val_boundary_output_iou_coef: 0.7923\n", "Epoch 422/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2765 - mask_output_loss: 0.1550 - boundary_output_loss: 0.2431 - mask_output_iou_coef: 0.8388 - boundary_output_iou_coef: 0.7081\n", "Epoch 422: val_loss improved from 0.18584 to 0.18193, saving model to final_model_instance_boundary.keras\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2765 - mask_output_loss: 0.1550 - boundary_output_loss: 0.2431 - mask_output_iou_coef: 0.8388 - boundary_output_iou_coef: 0.7081 - val_loss: 0.1819 - val_mask_output_loss: 0.0979 - val_boundary_output_loss: 0.1681 - val_mask_output_iou_coef: 0.8987 - val_boundary_output_iou_coef: 0.7960\n", "Epoch 423/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2856 - mask_output_loss: 0.1634 - boundary_output_loss: 0.2445 - mask_output_iou_coef: 0.8328 - boundary_output_iou_coef: 0.7056\n", "Epoch 423: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2856 - mask_output_loss: 0.1634 - boundary_output_loss: 0.2445 - mask_output_iou_coef: 0.8328 - boundary_output_iou_coef: 0.7056 - val_loss: 0.1901 - val_mask_output_loss: 0.1039 - val_boundary_output_loss: 0.1725 - val_mask_output_iou_coef: 0.8922 - val_boundary_output_iou_coef: 0.7918\n", "Epoch 424/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2803 - mask_output_loss: 0.1590 - boundary_output_loss: 0.2425 - mask_output_iou_coef: 0.8356 - boundary_output_iou_coef: 0.7081\n", "Epoch 424: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2803 - mask_output_loss: 0.1590 - boundary_output_loss: 0.2425 - mask_output_iou_coef: 0.8356 - boundary_output_iou_coef: 0.7081 - val_loss: 0.1895 - val_mask_output_loss: 0.1029 - val_boundary_output_loss: 0.1731 - val_mask_output_iou_coef: 0.8906 - val_boundary_output_iou_coef: 0.7899\n", "Epoch 425/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2758 - mask_output_loss: 0.1547 - boundary_output_loss: 0.2421 - mask_output_iou_coef: 0.8385 - boundary_output_iou_coef: 0.7091\n", "Epoch 425: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 547s 2s/step - loss: 0.2758 - mask_output_loss: 0.1547 - boundary_output_loss: 0.2421 - mask_output_iou_coef: 0.8385 - boundary_output_iou_coef: 0.7091 - val_loss: 0.1903 - val_mask_output_loss: 0.1039 - val_boundary_output_loss: 0.1727 - val_mask_output_iou_coef: 0.8958 - val_boundary_output_iou_coef: 0.7938\n", "Epoch 426/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2825 - mask_output_loss: 0.1604 - boundary_output_loss: 0.2443 - mask_output_iou_coef: 0.8347 - boundary_output_iou_coef: 0.7065\n", "Epoch 426: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2825 - mask_output_loss: 0.1604 - boundary_output_loss: 0.2443 - mask_output_iou_coef: 0.8347 - boundary_output_iou_coef: 0.7065 - val_loss: 0.1899 - val_mask_output_loss: 0.1031 - val_boundary_output_loss: 0.1737 - val_mask_output_iou_coef: 0.8950 - val_boundary_output_iou_coef: 0.7909\n", "Epoch 427/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2932 - mask_output_loss: 0.1696 - boundary_output_loss: 0.2473 - mask_output_iou_coef: 0.8278 - boundary_output_iou_coef: 0.7044\n", "Epoch 427: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 547s 2s/step - loss: 0.2932 - mask_output_loss: 0.1696 - boundary_output_loss: 0.2473 - mask_output_iou_coef: 0.8278 - boundary_output_iou_coef: 0.7044 - val_loss: 0.1977 - val_mask_output_loss: 0.1094 - val_boundary_output_loss: 0.1765 - val_mask_output_iou_coef: 0.8890 - val_boundary_output_iou_coef: 0.7880\n", "Epoch 428/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2851 - mask_output_loss: 0.1617 - boundary_output_loss: 0.2467 - mask_output_iou_coef: 0.8345 - boundary_output_iou_coef: 0.7042\n", "Epoch 428: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 545s 2s/step - loss: 0.2851 - mask_output_loss: 0.1617 - boundary_output_loss: 0.2467 - mask_output_iou_coef: 0.8345 - boundary_output_iou_coef: 0.7042 - val_loss: 0.1870 - val_mask_output_loss: 0.1010 - val_boundary_output_loss: 0.1720 - val_mask_output_iou_coef: 0.8945 - val_boundary_output_iou_coef: 0.7933\n", "Epoch 429/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2781 - mask_output_loss: 0.1560 - boundary_output_loss: 0.2443 - mask_output_iou_coef: 0.8386 - boundary_output_iou_coef: 0.7067\n", "Epoch 429: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2781 - mask_output_loss: 0.1560 - boundary_output_loss: 0.2443 - mask_output_iou_coef: 0.8386 - boundary_output_iou_coef: 0.7067 - val_loss: 0.1936 - val_mask_output_loss: 0.1051 - val_boundary_output_loss: 0.1769 - val_mask_output_iou_coef: 0.8941 - val_boundary_output_iou_coef: 0.7930\n", "Epoch 430/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2720 - mask_output_loss: 0.1528 - boundary_output_loss: 0.2384 - mask_output_iou_coef: 0.8410 - boundary_output_iou_coef: 0.7125\n", "Epoch 430: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 554s 2s/step - loss: 0.2720 - mask_output_loss: 0.1528 - boundary_output_loss: 0.2384 - mask_output_iou_coef: 0.8410 - boundary_output_iou_coef: 0.7125 - val_loss: 0.1966 - val_mask_output_loss: 0.1071 - val_boundary_output_loss: 0.1791 - val_mask_output_iou_coef: 0.8901 - val_boundary_output_iou_coef: 0.7862\n", "Epoch 431/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2752 - mask_output_loss: 0.1532 - boundary_output_loss: 0.2439 - mask_output_iou_coef: 0.8393 - boundary_output_iou_coef: 0.7067\n", "Epoch 431: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 545s 2s/step - loss: 0.2752 - mask_output_loss: 0.1532 - boundary_output_loss: 0.2439 - mask_output_iou_coef: 0.8393 - boundary_output_iou_coef: 0.7067 - val_loss: 0.1894 - val_mask_output_loss: 0.1024 - val_boundary_output_loss: 0.1740 - val_mask_output_iou_coef: 0.8944 - val_boundary_output_iou_coef: 0.7915\n", "Epoch 432/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2794 - mask_output_loss: 0.1564 - boundary_output_loss: 0.2460 - mask_output_iou_coef: 0.8371 - boundary_output_iou_coef: 0.7041\n", "Epoch 432: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 547s 2s/step - loss: 0.2794 - mask_output_loss: 0.1564 - boundary_output_loss: 0.2460 - mask_output_iou_coef: 0.8371 - boundary_output_iou_coef: 0.7041 - val_loss: 0.1913 - val_mask_output_loss: 0.1034 - val_boundary_output_loss: 0.1758 - val_mask_output_iou_coef: 0.8917 - val_boundary_output_iou_coef: 0.7884\n", "Epoch 433/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2715 - mask_output_loss: 0.1518 - boundary_output_loss: 0.2394 - mask_output_iou_coef: 0.8413 - boundary_output_iou_coef: 0.7116\n", "Epoch 433: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 546s 2s/step - loss: 0.2715 - mask_output_loss: 0.1518 - boundary_output_loss: 0.2394 - mask_output_iou_coef: 0.8413 - boundary_output_iou_coef: 0.7116 - val_loss: 0.1903 - val_mask_output_loss: 0.1039 - val_boundary_output_loss: 0.1728 - val_mask_output_iou_coef: 0.8930 - val_boundary_output_iou_coef: 0.7942\n", "Epoch 434/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2832 - mask_output_loss: 0.1610 - boundary_output_loss: 0.2444 - mask_output_iou_coef: 0.8346 - boundary_output_iou_coef: 0.7062\n", "Epoch 434: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2832 - mask_output_loss: 0.1610 - boundary_output_loss: 0.2444 - mask_output_iou_coef: 0.8346 - boundary_output_iou_coef: 0.7062 - val_loss: 0.1887 - val_mask_output_loss: 0.1027 - val_boundary_output_loss: 0.1721 - val_mask_output_iou_coef: 0.8938 - val_boundary_output_iou_coef: 0.7926\n", "Epoch 435/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2935 - mask_output_loss: 0.1699 - boundary_output_loss: 0.2472 - mask_output_iou_coef: 0.8297 - boundary_output_iou_coef: 0.7032\n", "Epoch 435: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2935 - mask_output_loss: 0.1699 - boundary_output_loss: 0.2472 - mask_output_iou_coef: 0.8297 - boundary_output_iou_coef: 0.7032 - val_loss: 0.1915 - val_mask_output_loss: 0.1038 - val_boundary_output_loss: 0.1754 - val_mask_output_iou_coef: 0.8914 - val_boundary_output_iou_coef: 0.7875\n", "Epoch 436/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2811 - mask_output_loss: 0.1622 - boundary_output_loss: 0.2379 - mask_output_iou_coef: 0.8338 - boundary_output_iou_coef: 0.7132\n", "Epoch 436: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2811 - mask_output_loss: 0.1622 - boundary_output_loss: 0.2379 - mask_output_iou_coef: 0.8338 - boundary_output_iou_coef: 0.7132 - val_loss: 0.1898 - val_mask_output_loss: 0.1036 - val_boundary_output_loss: 0.1724 - val_mask_output_iou_coef: 0.8940 - val_boundary_output_iou_coef: 0.7927\n", "Epoch 437/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2723 - mask_output_loss: 0.1539 - boundary_output_loss: 0.2366 - mask_output_iou_coef: 0.8398 - boundary_output_iou_coef: 0.7148\n", "Epoch 437: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 557s 2s/step - loss: 0.2723 - mask_output_loss: 0.1539 - boundary_output_loss: 0.2366 - mask_output_iou_coef: 0.8398 - boundary_output_iou_coef: 0.7148 - val_loss: 0.1896 - val_mask_output_loss: 0.1031 - val_boundary_output_loss: 0.1730 - val_mask_output_iou_coef: 0.8938 - val_boundary_output_iou_coef: 0.7905\n", "Epoch 438/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2794 - mask_output_loss: 0.1587 - boundary_output_loss: 0.2414 - mask_output_iou_coef: 0.8363 - boundary_output_iou_coef: 0.7092\n", "Epoch 438: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 559s 2s/step - loss: 0.2794 - mask_output_loss: 0.1587 - boundary_output_loss: 0.2414 - mask_output_iou_coef: 0.8363 - boundary_output_iou_coef: 0.7092 - val_loss: 0.1867 - val_mask_output_loss: 0.1016 - val_boundary_output_loss: 0.1703 - val_mask_output_iou_coef: 0.8936 - val_boundary_output_iou_coef: 0.7935\n", "Epoch 439/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2914 - mask_output_loss: 0.1666 - boundary_output_loss: 0.2495 - mask_output_iou_coef: 0.8294 - boundary_output_iou_coef: 0.7010\n", "Epoch 439: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 558s 2s/step - loss: 0.2914 - mask_output_loss: 0.1666 - boundary_output_loss: 0.2495 - mask_output_iou_coef: 0.8294 - boundary_output_iou_coef: 0.7010 - val_loss: 0.1898 - val_mask_output_loss: 0.1025 - val_boundary_output_loss: 0.1744 - val_mask_output_iou_coef: 0.8937 - val_boundary_output_iou_coef: 0.7902\n", "Epoch 440/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2762 - mask_output_loss: 0.1579 - boundary_output_loss: 0.2366 - mask_output_iou_coef: 0.8367 - boundary_output_iou_coef: 0.7145\n", "Epoch 440: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 556s 2s/step - loss: 0.2762 - mask_output_loss: 0.1579 - boundary_output_loss: 0.2366 - mask_output_iou_coef: 0.8367 - boundary_output_iou_coef: 0.7145 - val_loss: 0.1930 - val_mask_output_loss: 0.1046 - val_boundary_output_loss: 0.1770 - val_mask_output_iou_coef: 0.8870 - val_boundary_output_iou_coef: 0.7860\n", "Epoch 441/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2858 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8322 - boundary_output_iou_coef: 0.7054\n", "Epoch 441: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 556s 2s/step - loss: 0.2858 - mask_output_loss: 0.1630 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8322 - boundary_output_iou_coef: 0.7054 - val_loss: 0.1907 - val_mask_output_loss: 0.1037 - val_boundary_output_loss: 0.1742 - val_mask_output_iou_coef: 0.8935 - val_boundary_output_iou_coef: 0.7881\n", "Epoch 442/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2682 - mask_output_loss: 0.1478 - boundary_output_loss: 0.2408 - mask_output_iou_coef: 0.8451 - boundary_output_iou_coef: 0.7097\n", "Epoch 442: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2682 - mask_output_loss: 0.1478 - boundary_output_loss: 0.2408 - mask_output_iou_coef: 0.8451 - boundary_output_iou_coef: 0.7097 - val_loss: 0.1886 - val_mask_output_loss: 0.1021 - val_boundary_output_loss: 0.1731 - val_mask_output_iou_coef: 0.8947 - val_boundary_output_iou_coef: 0.7873\n", "Epoch 443/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2836 - mask_output_loss: 0.1628 - boundary_output_loss: 0.2416 - mask_output_iou_coef: 0.8330 - boundary_output_iou_coef: 0.7097\n", "Epoch 443: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2836 - mask_output_loss: 0.1628 - boundary_output_loss: 0.2416 - mask_output_iou_coef: 0.8330 - boundary_output_iou_coef: 0.7097 - val_loss: 0.1925 - val_mask_output_loss: 0.1049 - val_boundary_output_loss: 0.1752 - val_mask_output_iou_coef: 0.8932 - val_boundary_output_iou_coef: 0.7904\n", "Epoch 444/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2733 - mask_output_loss: 0.1538 - boundary_output_loss: 0.2391 - mask_output_iou_coef: 0.8391 - boundary_output_iou_coef: 0.7118\n", "Epoch 444: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 549s 2s/step - loss: 0.2733 - mask_output_loss: 0.1538 - boundary_output_loss: 0.2391 - mask_output_iou_coef: 0.8391 - boundary_output_iou_coef: 0.7118 - val_loss: 0.1924 - val_mask_output_loss: 0.1058 - val_boundary_output_loss: 0.1733 - val_mask_output_iou_coef: 0.8917 - val_boundary_output_iou_coef: 0.7939\n", "Epoch 445/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2888 - mask_output_loss: 0.1665 - boundary_output_loss: 0.2446 - mask_output_iou_coef: 0.8291 - boundary_output_iou_coef: 0.7063\n", "Epoch 445: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2888 - mask_output_loss: 0.1665 - boundary_output_loss: 0.2446 - mask_output_iou_coef: 0.8291 - boundary_output_iou_coef: 0.7063 - val_loss: 0.1898 - val_mask_output_loss: 0.1034 - val_boundary_output_loss: 0.1729 - val_mask_output_iou_coef: 0.8950 - val_boundary_output_iou_coef: 0.7927\n", "Epoch 446/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2776 - mask_output_loss: 0.1558 - boundary_output_loss: 0.2437 - mask_output_iou_coef: 0.8380 - boundary_output_iou_coef: 0.7064\n", "Epoch 446: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2776 - mask_output_loss: 0.1558 - boundary_output_loss: 0.2437 - mask_output_iou_coef: 0.8380 - boundary_output_iou_coef: 0.7064 - val_loss: 0.1919 - val_mask_output_loss: 0.1046 - val_boundary_output_loss: 0.1747 - val_mask_output_iou_coef: 0.8948 - val_boundary_output_iou_coef: 0.7916\n", "Epoch 447/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2785 - mask_output_loss: 0.1575 - boundary_output_loss: 0.2420 - mask_output_iou_coef: 0.8363 - boundary_output_iou_coef: 0.7087\n", "Epoch 447: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 556s 2s/step - loss: 0.2785 - mask_output_loss: 0.1575 - boundary_output_loss: 0.2420 - mask_output_iou_coef: 0.8363 - boundary_output_iou_coef: 0.7087 - val_loss: 0.1925 - val_mask_output_loss: 0.1051 - val_boundary_output_loss: 0.1748 - val_mask_output_iou_coef: 0.8938 - val_boundary_output_iou_coef: 0.7921\n", "Epoch 448/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2727 - mask_output_loss: 0.1523 - boundary_output_loss: 0.2408 - mask_output_iou_coef: 0.8399 - boundary_output_iou_coef: 0.7100\n", "Epoch 448: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 557s 2s/step - loss: 0.2727 - mask_output_loss: 0.1523 - boundary_output_loss: 0.2408 - mask_output_iou_coef: 0.8399 - boundary_output_iou_coef: 0.7100 - val_loss: 0.1980 - val_mask_output_loss: 0.1089 - val_boundary_output_loss: 0.1782 - val_mask_output_iou_coef: 0.8939 - val_boundary_output_iou_coef: 0.7900\n", "Epoch 449/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2865 - mask_output_loss: 0.1623 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8336 - boundary_output_iou_coef: 0.7023\n", "Epoch 449: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 560s 2s/step - loss: 0.2865 - mask_output_loss: 0.1623 - boundary_output_loss: 0.2484 - mask_output_iou_coef: 0.8336 - boundary_output_iou_coef: 0.7023 - val_loss: 0.1872 - val_mask_output_loss: 0.1011 - val_boundary_output_loss: 0.1722 - val_mask_output_iou_coef: 0.8943 - val_boundary_output_iou_coef: 0.7911\n", "Epoch 450/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2827 - mask_output_loss: 0.1600 - boundary_output_loss: 0.2453 - mask_output_iou_coef: 0.8347 - boundary_output_iou_coef: 0.7060\n", "Epoch 450: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2827 - mask_output_loss: 0.1600 - boundary_output_loss: 0.2453 - mask_output_iou_coef: 0.8347 - boundary_output_iou_coef: 0.7060 - val_loss: 0.1916 - val_mask_output_loss: 0.1028 - val_boundary_output_loss: 0.1775 - val_mask_output_iou_coef: 0.8929 - val_boundary_output_iou_coef: 0.7887\n", "Epoch 451/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2757 - mask_output_loss: 0.1560 - boundary_output_loss: 0.2394 - mask_output_iou_coef: 0.8390 - boundary_output_iou_coef: 0.7116\n", "Epoch 451: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2757 - mask_output_loss: 0.1560 - boundary_output_loss: 0.2394 - mask_output_iou_coef: 0.8390 - boundary_output_iou_coef: 0.7116 - val_loss: 0.1901 - val_mask_output_loss: 0.1028 - val_boundary_output_loss: 0.1746 - val_mask_output_iou_coef: 0.8935 - val_boundary_output_iou_coef: 0.7901\n", "Epoch 452/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2845 - mask_output_loss: 0.1618 - boundary_output_loss: 0.2454 - mask_output_iou_coef: 0.8334 - boundary_output_iou_coef: 0.7051\n", "Epoch 452: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 547s 2s/step - loss: 0.2845 - mask_output_loss: 0.1618 - boundary_output_loss: 0.2454 - mask_output_iou_coef: 0.8334 - boundary_output_iou_coef: 0.7051 - val_loss: 0.1972 - val_mask_output_loss: 0.1077 - val_boundary_output_loss: 0.1789 - val_mask_output_iou_coef: 0.8892 - val_boundary_output_iou_coef: 0.7856\n", "Epoch 453/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2755 - mask_output_loss: 0.1555 - boundary_output_loss: 0.2400 - mask_output_iou_coef: 0.8390 - boundary_output_iou_coef: 0.7115\n", "Epoch 453: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 536s 2s/step - loss: 0.2755 - mask_output_loss: 0.1555 - boundary_output_loss: 0.2400 - mask_output_iou_coef: 0.8390 - boundary_output_iou_coef: 0.7115 - val_loss: 0.1911 - val_mask_output_loss: 0.1026 - val_boundary_output_loss: 0.1770 - val_mask_output_iou_coef: 0.8948 - val_boundary_output_iou_coef: 0.7878\n", "Epoch 454/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2783 - mask_output_loss: 0.1578 - boundary_output_loss: 0.2411 - mask_output_iou_coef: 0.8381 - boundary_output_iou_coef: 0.7099\n", "Epoch 454: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 539s 2s/step - loss: 0.2783 - mask_output_loss: 0.1578 - boundary_output_loss: 0.2411 - mask_output_iou_coef: 0.8381 - boundary_output_iou_coef: 0.7099 - val_loss: 0.1858 - val_mask_output_loss: 0.0998 - val_boundary_output_loss: 0.1720 - val_mask_output_iou_coef: 0.8955 - val_boundary_output_iou_coef: 0.7935\n", "Epoch 455/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2855 - mask_output_loss: 0.1633 - boundary_output_loss: 0.2444 - mask_output_iou_coef: 0.8333 - boundary_output_iou_coef: 0.7078\n", "Epoch 455: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 539s 2s/step - loss: 0.2855 - mask_output_loss: 0.1633 - boundary_output_loss: 0.2444 - mask_output_iou_coef: 0.8333 - boundary_output_iou_coef: 0.7078 - val_loss: 0.1883 - val_mask_output_loss: 0.1021 - val_boundary_output_loss: 0.1726 - val_mask_output_iou_coef: 0.8918 - val_boundary_output_iou_coef: 0.7915\n", "Epoch 456/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2921 - mask_output_loss: 0.1682 - boundary_output_loss: 0.2477 - mask_output_iou_coef: 0.8282 - boundary_output_iou_coef: 0.7034\n", "Epoch 456: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 541s 2s/step - loss: 0.2921 - mask_output_loss: 0.1682 - boundary_output_loss: 0.2477 - mask_output_iou_coef: 0.8282 - boundary_output_iou_coef: 0.7034 - val_loss: 0.1930 - val_mask_output_loss: 0.1055 - val_boundary_output_loss: 0.1751 - val_mask_output_iou_coef: 0.8914 - val_boundary_output_iou_coef: 0.7902\n", "Epoch 457/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2958 - mask_output_loss: 0.1730 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8251 - boundary_output_iou_coef: 0.7055\n", "Epoch 457: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2958 - mask_output_loss: 0.1730 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8251 - boundary_output_iou_coef: 0.7055 - val_loss: 0.1932 - val_mask_output_loss: 0.1050 - val_boundary_output_loss: 0.1765 - val_mask_output_iou_coef: 0.8948 - val_boundary_output_iou_coef: 0.7915\n", "Epoch 458/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2865 - mask_output_loss: 0.1655 - boundary_output_loss: 0.2420 - mask_output_iou_coef: 0.8312 - boundary_output_iou_coef: 0.7093\n", "Epoch 458: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 546s 2s/step - loss: 0.2865 - mask_output_loss: 0.1655 - boundary_output_loss: 0.2420 - mask_output_iou_coef: 0.8312 - boundary_output_iou_coef: 0.7093 - val_loss: 0.1911 - val_mask_output_loss: 0.1036 - val_boundary_output_loss: 0.1750 - val_mask_output_iou_coef: 0.8942 - val_boundary_output_iou_coef: 0.7911\n", "Epoch 459/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2785 - mask_output_loss: 0.1576 - boundary_output_loss: 0.2418 - mask_output_iou_coef: 0.8370 - boundary_output_iou_coef: 0.7096\n", "Epoch 459: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2785 - mask_output_loss: 0.1576 - boundary_output_loss: 0.2418 - mask_output_iou_coef: 0.8370 - boundary_output_iou_coef: 0.7096 - val_loss: 0.1916 - val_mask_output_loss: 0.1046 - val_boundary_output_loss: 0.1741 - val_mask_output_iou_coef: 0.8920 - val_boundary_output_iou_coef: 0.7896\n", "Epoch 460/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2783 - mask_output_loss: 0.1571 - boundary_output_loss: 0.2425 - mask_output_iou_coef: 0.8381 - boundary_output_iou_coef: 0.7087\n", "Epoch 460: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 543s 2s/step - loss: 0.2783 - mask_output_loss: 0.1571 - boundary_output_loss: 0.2425 - mask_output_iou_coef: 0.8381 - boundary_output_iou_coef: 0.7087 - val_loss: 0.1908 - val_mask_output_loss: 0.1035 - val_boundary_output_loss: 0.1746 - val_mask_output_iou_coef: 0.8960 - val_boundary_output_iou_coef: 0.7900\n", "Epoch 461/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2764 - mask_output_loss: 0.1556 - boundary_output_loss: 0.2416 - mask_output_iou_coef: 0.8381 - boundary_output_iou_coef: 0.7086\n", "Epoch 461: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2764 - mask_output_loss: 0.1556 - boundary_output_loss: 0.2416 - mask_output_iou_coef: 0.8381 - boundary_output_iou_coef: 0.7086 - val_loss: 0.1922 - val_mask_output_loss: 0.1031 - val_boundary_output_loss: 0.1782 - val_mask_output_iou_coef: 0.8954 - val_boundary_output_iou_coef: 0.7908\n", "Epoch 462/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2927 - mask_output_loss: 0.1704 - boundary_output_loss: 0.2445 - mask_output_iou_coef: 0.8294 - boundary_output_iou_coef: 0.7064\n", "Epoch 462: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 543s 2s/step - loss: 0.2927 - mask_output_loss: 0.1704 - boundary_output_loss: 0.2445 - mask_output_iou_coef: 0.8294 - boundary_output_iou_coef: 0.7064 - val_loss: 0.1882 - val_mask_output_loss: 0.1027 - val_boundary_output_loss: 0.1711 - val_mask_output_iou_coef: 0.8908 - val_boundary_output_iou_coef: 0.7916\n", "Epoch 463/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2831 - mask_output_loss: 0.1620 - boundary_output_loss: 0.2422 - mask_output_iou_coef: 0.8334 - boundary_output_iou_coef: 0.7081\n", "Epoch 463: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 546s 2s/step - loss: 0.2831 - mask_output_loss: 0.1620 - boundary_output_loss: 0.2422 - mask_output_iou_coef: 0.8334 - boundary_output_iou_coef: 0.7081 - val_loss: 0.1930 - val_mask_output_loss: 0.1046 - val_boundary_output_loss: 0.1769 - val_mask_output_iou_coef: 0.8917 - val_boundary_output_iou_coef: 0.7900\n", "Epoch 464/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.3229 - mask_output_loss: 0.1924 - boundary_output_loss: 0.2609 - mask_output_iou_coef: 0.8174 - boundary_output_iou_coef: 0.6925\n", "Epoch 464: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 549s 2s/step - loss: 0.3229 - mask_output_loss: 0.1924 - boundary_output_loss: 0.2609 - mask_output_iou_coef: 0.8174 - boundary_output_iou_coef: 0.6925 - val_loss: 0.1951 - val_mask_output_loss: 0.1052 - val_boundary_output_loss: 0.1799 - val_mask_output_iou_coef: 0.8910 - val_boundary_output_iou_coef: 0.7872\n", "Epoch 465/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2809 - mask_output_loss: 0.1596 - boundary_output_loss: 0.2424 - mask_output_iou_coef: 0.8350 - boundary_output_iou_coef: 0.7091\n", "Epoch 465: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 542s 2s/step - loss: 0.2809 - mask_output_loss: 0.1596 - boundary_output_loss: 0.2424 - mask_output_iou_coef: 0.8350 - boundary_output_iou_coef: 0.7091 - val_loss: 0.1911 - val_mask_output_loss: 0.1041 - val_boundary_output_loss: 0.1740 - val_mask_output_iou_coef: 0.8917 - val_boundary_output_iou_coef: 0.7917\n", "Epoch 466/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2822 - mask_output_loss: 0.1594 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8353 - boundary_output_iou_coef: 0.7049\n", "Epoch 466: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2822 - mask_output_loss: 0.1594 - boundary_output_loss: 0.2456 - mask_output_iou_coef: 0.8353 - boundary_output_iou_coef: 0.7049 - val_loss: 0.1878 - val_mask_output_loss: 0.1014 - val_boundary_output_loss: 0.1726 - val_mask_output_iou_coef: 0.8924 - val_boundary_output_iou_coef: 0.7880\n", "Epoch 467/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2826 - mask_output_loss: 0.1604 - boundary_output_loss: 0.2442 - mask_output_iou_coef: 0.8339 - boundary_output_iou_coef: 0.7067\n", "Epoch 467: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 555s 2s/step - loss: 0.2826 - mask_output_loss: 0.1604 - boundary_output_loss: 0.2442 - mask_output_iou_coef: 0.8339 - boundary_output_iou_coef: 0.7067 - val_loss: 0.1930 - val_mask_output_loss: 0.1046 - val_boundary_output_loss: 0.1769 - val_mask_output_iou_coef: 0.8926 - val_boundary_output_iou_coef: 0.7876\n", "Epoch 468/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2702 - mask_output_loss: 0.1504 - boundary_output_loss: 0.2394 - mask_output_iou_coef: 0.8429 - boundary_output_iou_coef: 0.7113\n", "Epoch 468: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 558s 2s/step - loss: 0.2702 - mask_output_loss: 0.1504 - boundary_output_loss: 0.2394 - mask_output_iou_coef: 0.8429 - boundary_output_iou_coef: 0.7113 - val_loss: 0.1862 - val_mask_output_loss: 0.1003 - val_boundary_output_loss: 0.1718 - val_mask_output_iou_coef: 0.8954 - val_boundary_output_iou_coef: 0.7938\n", "Epoch 469/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2797 - mask_output_loss: 0.1592 - boundary_output_loss: 0.2410 - mask_output_iou_coef: 0.8367 - boundary_output_iou_coef: 0.7104\n", "Epoch 469: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 558s 2s/step - loss: 0.2797 - mask_output_loss: 0.1592 - boundary_output_loss: 0.2410 - mask_output_iou_coef: 0.8367 - boundary_output_iou_coef: 0.7104 - val_loss: 0.1877 - val_mask_output_loss: 0.1015 - val_boundary_output_loss: 0.1724 - val_mask_output_iou_coef: 0.8932 - val_boundary_output_iou_coef: 0.7909\n", "Epoch 470/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2788 - mask_output_loss: 0.1572 - boundary_output_loss: 0.2433 - mask_output_iou_coef: 0.8369 - boundary_output_iou_coef: 0.7073\n", "Epoch 470: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 561s 2s/step - loss: 0.2788 - mask_output_loss: 0.1572 - boundary_output_loss: 0.2433 - mask_output_iou_coef: 0.8369 - boundary_output_iou_coef: 0.7073 - val_loss: 0.1911 - val_mask_output_loss: 0.1039 - val_boundary_output_loss: 0.1744 - val_mask_output_iou_coef: 0.8921 - val_boundary_output_iou_coef: 0.7891\n", "Epoch 471/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2853 - mask_output_loss: 0.1650 - boundary_output_loss: 0.2407 - mask_output_iou_coef: 0.8311 - boundary_output_iou_coef: 0.7101\n", "Epoch 471: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 561s 2s/step - loss: 0.2853 - mask_output_loss: 0.1650 - boundary_output_loss: 0.2407 - mask_output_iou_coef: 0.8311 - boundary_output_iou_coef: 0.7101 - val_loss: 0.1902 - val_mask_output_loss: 0.1038 - val_boundary_output_loss: 0.1728 - val_mask_output_iou_coef: 0.8942 - val_boundary_output_iou_coef: 0.7913\n", "Epoch 472/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2722 - mask_output_loss: 0.1548 - boundary_output_loss: 0.2348 - mask_output_iou_coef: 0.8392 - boundary_output_iou_coef: 0.7165\n", "Epoch 472: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2722 - mask_output_loss: 0.1548 - boundary_output_loss: 0.2348 - mask_output_iou_coef: 0.8392 - boundary_output_iou_coef: 0.7165 - val_loss: 0.1896 - val_mask_output_loss: 0.1031 - val_boundary_output_loss: 0.1731 - val_mask_output_iou_coef: 0.8939 - val_boundary_output_iou_coef: 0.7917\n", "Epoch 473/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2868 - mask_output_loss: 0.1628 - boundary_output_loss: 0.2481 - mask_output_iou_coef: 0.8315 - boundary_output_iou_coef: 0.7030\n", "Epoch 473: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 544s 2s/step - loss: 0.2868 - mask_output_loss: 0.1628 - boundary_output_loss: 0.2481 - mask_output_iou_coef: 0.8315 - boundary_output_iou_coef: 0.7030 - val_loss: 0.1883 - val_mask_output_loss: 0.1016 - val_boundary_output_loss: 0.1734 - val_mask_output_iou_coef: 0.8948 - val_boundary_output_iou_coef: 0.7934\n", "Epoch 474/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2811 - mask_output_loss: 0.1585 - boundary_output_loss: 0.2453 - mask_output_iou_coef: 0.8365 - boundary_output_iou_coef: 0.7051\n", "Epoch 474: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 554s 2s/step - loss: 0.2811 - mask_output_loss: 0.1585 - boundary_output_loss: 0.2453 - mask_output_iou_coef: 0.8365 - boundary_output_iou_coef: 0.7051 - val_loss: 0.1850 - val_mask_output_loss: 0.0988 - val_boundary_output_loss: 0.1724 - val_mask_output_iou_coef: 0.8959 - val_boundary_output_iou_coef: 0.7944\n", "Epoch 475/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2840 - mask_output_loss: 0.1636 - boundary_output_loss: 0.2408 - mask_output_iou_coef: 0.8327 - boundary_output_iou_coef: 0.7106\n", "Epoch 475: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2840 - mask_output_loss: 0.1636 - boundary_output_loss: 0.2408 - mask_output_iou_coef: 0.8327 - boundary_output_iou_coef: 0.7106 - val_loss: 0.1913 - val_mask_output_loss: 0.1044 - val_boundary_output_loss: 0.1736 - val_mask_output_iou_coef: 0.8915 - val_boundary_output_iou_coef: 0.7921\n", "Epoch 476/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2860 - mask_output_loss: 0.1640 - boundary_output_loss: 0.2440 - mask_output_iou_coef: 0.8324 - boundary_output_iou_coef: 0.7068\n", "Epoch 476: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 549s 2s/step - loss: 0.2860 - mask_output_loss: 0.1640 - boundary_output_loss: 0.2440 - mask_output_iou_coef: 0.8324 - boundary_output_iou_coef: 0.7068 - val_loss: 0.1870 - val_mask_output_loss: 0.1014 - val_boundary_output_loss: 0.1713 - val_mask_output_iou_coef: 0.8953 - val_boundary_output_iou_coef: 0.7932\n", "Epoch 477/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2761 - mask_output_loss: 0.1563 - boundary_output_loss: 0.2396 - mask_output_iou_coef: 0.8398 - boundary_output_iou_coef: 0.7109\n", "Epoch 477: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 546s 2s/step - loss: 0.2761 - mask_output_loss: 0.1563 - boundary_output_loss: 0.2396 - mask_output_iou_coef: 0.8398 - boundary_output_iou_coef: 0.7109 - val_loss: 0.1944 - val_mask_output_loss: 0.1071 - val_boundary_output_loss: 0.1747 - val_mask_output_iou_coef: 0.8860 - val_boundary_output_iou_coef: 0.7879\n", "Epoch 478/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2687 - mask_output_loss: 0.1497 - boundary_output_loss: 0.2381 - mask_output_iou_coef: 0.8437 - boundary_output_iou_coef: 0.7134\n", "Epoch 478: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 551s 2s/step - loss: 0.2687 - mask_output_loss: 0.1497 - boundary_output_loss: 0.2381 - mask_output_iou_coef: 0.8437 - boundary_output_iou_coef: 0.7134 - val_loss: 0.1908 - val_mask_output_loss: 0.1047 - val_boundary_output_loss: 0.1722 - val_mask_output_iou_coef: 0.8937 - val_boundary_output_iou_coef: 0.7951\n", "Epoch 479/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2840 - mask_output_loss: 0.1636 - boundary_output_loss: 0.2408 - mask_output_iou_coef: 0.8329 - boundary_output_iou_coef: 0.7102\n", "Epoch 479: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2840 - mask_output_loss: 0.1636 - boundary_output_loss: 0.2408 - mask_output_iou_coef: 0.8329 - boundary_output_iou_coef: 0.7102 - val_loss: 0.1881 - val_mask_output_loss: 0.1025 - val_boundary_output_loss: 0.1712 - val_mask_output_iou_coef: 0.8950 - val_boundary_output_iou_coef: 0.7927\n", "Epoch 480/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2824 - mask_output_loss: 0.1631 - boundary_output_loss: 0.2387 - mask_output_iou_coef: 0.8325 - boundary_output_iou_coef: 0.7130\n", "Epoch 480: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2824 - mask_output_loss: 0.1631 - boundary_output_loss: 0.2387 - mask_output_iou_coef: 0.8325 - boundary_output_iou_coef: 0.7130 - val_loss: 0.1859 - val_mask_output_loss: 0.1000 - val_boundary_output_loss: 0.1718 - val_mask_output_iou_coef: 0.8932 - val_boundary_output_iou_coef: 0.7920\n", "Epoch 481/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2824 - mask_output_loss: 0.1590 - boundary_output_loss: 0.2469 - mask_output_iou_coef: 0.8350 - boundary_output_iou_coef: 0.7042\n", "Epoch 481: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 556s 2s/step - loss: 0.2824 - mask_output_loss: 0.1590 - boundary_output_loss: 0.2469 - mask_output_iou_coef: 0.8350 - boundary_output_iou_coef: 0.7042 - val_loss: 0.1950 - val_mask_output_loss: 0.1054 - val_boundary_output_loss: 0.1794 - val_mask_output_iou_coef: 0.8937 - val_boundary_output_iou_coef: 0.7871\n", "Epoch 482/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2757 - mask_output_loss: 0.1518 - boundary_output_loss: 0.2478 - mask_output_iou_coef: 0.8407 - boundary_output_iou_coef: 0.7028\n", "Epoch 482: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 557s 2s/step - loss: 0.2757 - mask_output_loss: 0.1518 - boundary_output_loss: 0.2478 - mask_output_iou_coef: 0.8407 - boundary_output_iou_coef: 0.7028 - val_loss: 0.1916 - val_mask_output_loss: 0.1036 - val_boundary_output_loss: 0.1760 - val_mask_output_iou_coef: 0.8909 - val_boundary_output_iou_coef: 0.7867\n", "Epoch 483/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2764 - mask_output_loss: 0.1541 - boundary_output_loss: 0.2446 - mask_output_iou_coef: 0.8395 - boundary_output_iou_coef: 0.7062\n", "Epoch 483: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 562s 2s/step - loss: 0.2764 - mask_output_loss: 0.1541 - boundary_output_loss: 0.2446 - mask_output_iou_coef: 0.8395 - boundary_output_iou_coef: 0.7062 - val_loss: 0.1874 - val_mask_output_loss: 0.1015 - val_boundary_output_loss: 0.1717 - val_mask_output_iou_coef: 0.8959 - val_boundary_output_iou_coef: 0.7913\n", "Epoch 484/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2809 - mask_output_loss: 0.1573 - boundary_output_loss: 0.2471 - mask_output_iou_coef: 0.8375 - boundary_output_iou_coef: 0.7045\n", "Epoch 484: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 560s 2s/step - loss: 0.2809 - mask_output_loss: 0.1573 - boundary_output_loss: 0.2471 - mask_output_iou_coef: 0.8375 - boundary_output_iou_coef: 0.7045 - val_loss: 0.1939 - val_mask_output_loss: 0.1070 - val_boundary_output_loss: 0.1739 - val_mask_output_iou_coef: 0.8900 - val_boundary_output_iou_coef: 0.7900\n", "Epoch 485/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2842 - mask_output_loss: 0.1608 - boundary_output_loss: 0.2468 - mask_output_iou_coef: 0.8359 - boundary_output_iou_coef: 0.7043\n", "Epoch 485: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 557s 2s/step - loss: 0.2842 - mask_output_loss: 0.1608 - boundary_output_loss: 0.2468 - mask_output_iou_coef: 0.8359 - boundary_output_iou_coef: 0.7043 - val_loss: 0.1880 - val_mask_output_loss: 0.1018 - val_boundary_output_loss: 0.1724 - val_mask_output_iou_coef: 0.8940 - val_boundary_output_iou_coef: 0.7945\n", "Epoch 486/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2902 - mask_output_loss: 0.1666 - boundary_output_loss: 0.2472 - mask_output_iou_coef: 0.8314 - boundary_output_iou_coef: 0.7047\n", "Epoch 486: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 556s 2s/step - loss: 0.2902 - mask_output_loss: 0.1666 - boundary_output_loss: 0.2472 - mask_output_iou_coef: 0.8314 - boundary_output_iou_coef: 0.7047 - val_loss: 0.1911 - val_mask_output_loss: 0.1043 - val_boundary_output_loss: 0.1736 - val_mask_output_iou_coef: 0.8924 - val_boundary_output_iou_coef: 0.7906\n", "Epoch 487/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2757 - mask_output_loss: 0.1558 - boundary_output_loss: 0.2398 - mask_output_iou_coef: 0.8373 - boundary_output_iou_coef: 0.7113\n", "Epoch 487: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 560s 2s/step - loss: 0.2757 - mask_output_loss: 0.1558 - boundary_output_loss: 0.2398 - mask_output_iou_coef: 0.8373 - boundary_output_iou_coef: 0.7113 - val_loss: 0.1902 - val_mask_output_loss: 0.1037 - val_boundary_output_loss: 0.1730 - val_mask_output_iou_coef: 0.8928 - val_boundary_output_iou_coef: 0.7925\n", "Epoch 488/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2694 - mask_output_loss: 0.1504 - boundary_output_loss: 0.2382 - mask_output_iou_coef: 0.8433 - boundary_output_iou_coef: 0.7134\n", "Epoch 488: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 558s 2s/step - loss: 0.2694 - mask_output_loss: 0.1504 - boundary_output_loss: 0.2382 - mask_output_iou_coef: 0.8433 - boundary_output_iou_coef: 0.7134 - val_loss: 0.1881 - val_mask_output_loss: 0.1025 - val_boundary_output_loss: 0.1712 - val_mask_output_iou_coef: 0.8944 - val_boundary_output_iou_coef: 0.7960\n", "Epoch 489/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2746 - mask_output_loss: 0.1545 - boundary_output_loss: 0.2401 - mask_output_iou_coef: 0.8400 - boundary_output_iou_coef: 0.7110\n", "Epoch 489: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2746 - mask_output_loss: 0.1545 - boundary_output_loss: 0.2401 - mask_output_iou_coef: 0.8400 - boundary_output_iou_coef: 0.7110 - val_loss: 0.1899 - val_mask_output_loss: 0.1031 - val_boundary_output_loss: 0.1737 - val_mask_output_iou_coef: 0.8909 - val_boundary_output_iou_coef: 0.7897\n", "Epoch 490/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2717 - mask_output_loss: 0.1520 - boundary_output_loss: 0.2393 - mask_output_iou_coef: 0.8420 - boundary_output_iou_coef: 0.7120\n", "Epoch 490: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 545s 2s/step - loss: 0.2717 - mask_output_loss: 0.1520 - boundary_output_loss: 0.2393 - mask_output_iou_coef: 0.8420 - boundary_output_iou_coef: 0.7120 - val_loss: 0.1890 - val_mask_output_loss: 0.1017 - val_boundary_output_loss: 0.1745 - val_mask_output_iou_coef: 0.8952 - val_boundary_output_iou_coef: 0.7914\n", "Epoch 491/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2817 - mask_output_loss: 0.1603 - boundary_output_loss: 0.2429 - mask_output_iou_coef: 0.8348 - boundary_output_iou_coef: 0.7085\n", "Epoch 491: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 548s 2s/step - loss: 0.2817 - mask_output_loss: 0.1603 - boundary_output_loss: 0.2429 - mask_output_iou_coef: 0.8348 - boundary_output_iou_coef: 0.7085 - val_loss: 0.1843 - val_mask_output_loss: 0.0996 - val_boundary_output_loss: 0.1695 - val_mask_output_iou_coef: 0.8976 - val_boundary_output_iou_coef: 0.7945\n", "Epoch 492/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2751 - mask_output_loss: 0.1553 - boundary_output_loss: 0.2396 - mask_output_iou_coef: 0.8386 - boundary_output_iou_coef: 0.7115\n", "Epoch 492: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 545s 2s/step - loss: 0.2751 - mask_output_loss: 0.1553 - boundary_output_loss: 0.2396 - mask_output_iou_coef: 0.8386 - boundary_output_iou_coef: 0.7115 - val_loss: 0.1886 - val_mask_output_loss: 0.1027 - val_boundary_output_loss: 0.1718 - val_mask_output_iou_coef: 0.8946 - val_boundary_output_iou_coef: 0.7904\n", "Epoch 493/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2702 - mask_output_loss: 0.1517 - boundary_output_loss: 0.2370 - mask_output_iou_coef: 0.8416 - boundary_output_iou_coef: 0.7138\n", "Epoch 493: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 547s 2s/step - loss: 0.2702 - mask_output_loss: 0.1517 - boundary_output_loss: 0.2370 - mask_output_iou_coef: 0.8416 - boundary_output_iou_coef: 0.7138 - val_loss: 0.1856 - val_mask_output_loss: 0.1005 - val_boundary_output_loss: 0.1703 - val_mask_output_iou_coef: 0.8952 - val_boundary_output_iou_coef: 0.7958\n", "Epoch 494/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2812 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2383 - mask_output_iou_coef: 0.8345 - boundary_output_iou_coef: 0.7132\n", "Epoch 494: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 549s 2s/step - loss: 0.2812 - mask_output_loss: 0.1621 - boundary_output_loss: 0.2383 - mask_output_iou_coef: 0.8345 - boundary_output_iou_coef: 0.7132 - val_loss: 0.1891 - val_mask_output_loss: 0.1033 - val_boundary_output_loss: 0.1717 - val_mask_output_iou_coef: 0.8924 - val_boundary_output_iou_coef: 0.7931\n", "Epoch 495/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2743 - mask_output_loss: 0.1549 - boundary_output_loss: 0.2387 - mask_output_iou_coef: 0.8384 - boundary_output_iou_coef: 0.7125\n", "Epoch 495: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2743 - mask_output_loss: 0.1549 - boundary_output_loss: 0.2387 - mask_output_iou_coef: 0.8384 - boundary_output_iou_coef: 0.7125 - val_loss: 0.1866 - val_mask_output_loss: 0.1012 - val_boundary_output_loss: 0.1707 - val_mask_output_iou_coef: 0.8914 - val_boundary_output_iou_coef: 0.7927\n", "Epoch 496/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2706 - mask_output_loss: 0.1526 - boundary_output_loss: 0.2360 - mask_output_iou_coef: 0.8414 - boundary_output_iou_coef: 0.7154\n", "Epoch 496: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2706 - mask_output_loss: 0.1526 - boundary_output_loss: 0.2360 - mask_output_iou_coef: 0.8414 - boundary_output_iou_coef: 0.7154 - val_loss: 0.1879 - val_mask_output_loss: 0.1019 - val_boundary_output_loss: 0.1720 - val_mask_output_iou_coef: 0.8955 - val_boundary_output_iou_coef: 0.7945\n", "Epoch 497/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2774 - mask_output_loss: 0.1575 - boundary_output_loss: 0.2397 - mask_output_iou_coef: 0.8391 - boundary_output_iou_coef: 0.7112\n", "Epoch 497: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 552s 2s/step - loss: 0.2774 - mask_output_loss: 0.1575 - boundary_output_loss: 0.2397 - mask_output_iou_coef: 0.8391 - boundary_output_iou_coef: 0.7112 - val_loss: 0.1835 - val_mask_output_loss: 0.0991 - val_boundary_output_loss: 0.1688 - val_mask_output_iou_coef: 0.8953 - val_boundary_output_iou_coef: 0.7962\n", "Epoch 498/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2741 - mask_output_loss: 0.1535 - boundary_output_loss: 0.2411 - mask_output_iou_coef: 0.8398 - boundary_output_iou_coef: 0.7101\n", "Epoch 498: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 550s 2s/step - loss: 0.2741 - mask_output_loss: 0.1535 - boundary_output_loss: 0.2411 - mask_output_iou_coef: 0.8398 - boundary_output_iou_coef: 0.7101 - val_loss: 0.1918 - val_mask_output_loss: 0.1043 - val_boundary_output_loss: 0.1751 - val_mask_output_iou_coef: 0.8944 - val_boundary_output_iou_coef: 0.7930\n", "Epoch 499/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2800 - mask_output_loss: 0.1588 - boundary_output_loss: 0.2424 - mask_output_iou_coef: 0.8363 - boundary_output_iou_coef: 0.7087\n", "Epoch 499: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 555s 2s/step - loss: 0.2800 - mask_output_loss: 0.1588 - boundary_output_loss: 0.2424 - mask_output_iou_coef: 0.8363 - boundary_output_iou_coef: 0.7087 - val_loss: 0.1852 - val_mask_output_loss: 0.0997 - val_boundary_output_loss: 0.1710 - val_mask_output_iou_coef: 0.8984 - val_boundary_output_iou_coef: 0.7936\n", "Epoch 500/500\n", "230/230 [==============================] - ETA: 0s - loss: 0.2759 - mask_output_loss: 0.1546 - boundary_output_loss: 0.2426 - mask_output_iou_coef: 0.8403 - boundary_output_iou_coef: 0.7074\n", "Epoch 500: val_loss did not improve from 0.18193\n", "230/230 [==============================] - 553s 2s/step - loss: 0.2759 - mask_output_loss: 0.1546 - boundary_output_loss: 0.2426 - mask_output_iou_coef: 0.8403 - boundary_output_iou_coef: 0.7074 - val_loss: 0.1863 - val_mask_output_loss: 0.1012 - val_boundary_output_loss: 0.1701 - val_mask_output_iou_coef: 0.8979 - val_boundary_output_iou_coef: 0.7956\n", "\n", "✅ Training complete. Best model loaded.\n" ] } ], "source": [ "# -------------------------\n", "# 5. U-Net Architecture\n", "# -------------------------\n", "def unet_multi_output(input_size=(256, 256, 3), dropout_rate=0.3):\n", " inputs = tf.keras.Input(shape=input_size)\n", "\n", " c1 = layers.Conv2D(32, 3, activation='relu', padding='same')(inputs)\n", " c1 = layers.Conv2D(32, 3, activation='relu', padding='same')(c1)\n", " p1 = layers.MaxPooling2D(2)(c1)\n", " p1 = layers.Dropout(dropout_rate)(p1)\n", "\n", " c2 = layers.Conv2D(64, 3, activation='relu', padding='same')(p1)\n", " c2 = layers.Conv2D(64, 3, activation='relu', padding='same')(c2)\n", " p2 = layers.MaxPooling2D(2)(c2)\n", " p2 = layers.Dropout(dropout_rate)(p2)\n", "\n", " c3 = layers.Conv2D(128, 3, activation='relu', padding='same')(p2)\n", " c3 = layers.Conv2D(128, 3, activation='relu', padding='same')(c3)\n", " c3 = layers.Dropout(dropout_rate)(c3)\n", "\n", " u4 = layers.UpSampling2D(2)(c3)\n", " concat4 = layers.Concatenate()([u4, c2])\n", " c4 = layers.Conv2D(64, 3, activation='relu', padding='same')(concat4)\n", " c4 = layers.Conv2D(64, 3, activation='relu', padding='same')(c4)\n", " c4 = layers.Dropout(dropout_rate)(c4)\n", "\n", " u5 = layers.UpSampling2D(2)(c4)\n", " concat5 = layers.Concatenate()([u5, c1])\n", " c5 = layers.Conv2D(32, 3, activation='relu', padding='same')(concat5)\n", " c5 = layers.Conv2D(32, 3, activation='relu', padding='same')(c5)\n", " c5 = layers.Dropout(dropout_rate)(c5)\n", "\n", " mask_output = layers.Conv2D(1, 1, activation='sigmoid', name=\"mask_output\")(c5)\n", " boundary_output = layers.Conv2D(1, 1, activation='sigmoid', name=\"boundary_output\")(c5)\n", "\n", " return Model(inputs, [mask_output, boundary_output])\n", "\n", "# -------------------------\n", "# 6. Train Model\n", "# -------------------------\n", "all_image_files = [f for f in os.listdir(images_dir) if f.lower().endswith(('.tif', '.png', '.jpg'))]\n", "all_image_paths = [os.path.join(images_dir, f) for f in all_image_files]\n", "all_mask_paths = [os.path.join(masks_dir, f) for f in all_image_files]\n", "\n", "print(f\"\\n📊 Total images: {len(all_image_paths)}\")\n", "print(f\"📊 Total masks: {len(all_mask_paths)}\")\n", "\n", "train_imgs, val_imgs, train_masks, val_masks = train_test_split(\n", " all_image_paths, all_mask_paths, test_size=0.2, random_state=42)\n", "\n", "print(f\"📂 Training: {len(train_imgs)}\")\n", "print(f\"📂 Validation: {len(val_imgs)}\")\n", "\n", "train_ds = create_dataset(train_imgs, train_masks, BATCH_SIZE, augment_flag=True)\n", "val_ds = create_dataset(val_imgs, val_masks, BATCH_SIZE)\n", "\n", "model = unet_multi_output()\n", "model.compile(\n", " optimizer='adam',\n", " loss={\"mask_output\": bce_dice_loss, \"boundary_output\": bce_dice_loss},\n", " loss_weights={\"mask_output\": 1.0, \"boundary_output\": 0.5},\n", " metrics={\"mask_output\": iou_coef, \"boundary_output\": iou_coef}\n", ")\n", "\n", "checkpoint_cb = callbacks.ModelCheckpoint(\n", " \"final_model_instance_boundary.keras\", monitor=\"val_loss\", save_best_only=True, verbose=1)\n", "\n", "history = model.fit(\n", " train_ds,\n", " validation_data=val_ds,\n", " epochs=EPOCHS,\n", " callbacks=[checkpoint_cb],\n", " verbose=1\n", ")\n", "\n", "model = tf.keras.models.load_model(\n", " \"final_model_instance_boundary.keras\",\n", " custom_objects={\"iou_coef\": iou_coef, \"bce_dice_loss\": bce_dice_loss}\n", ")\n", "\n", "print(\"\\n✅ Training complete. Best model loaded.\")\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -------------------------\n", "# 7. Plotting\n", "# -------------------------\n", "history_dict = history.history\n", "epochs = range(1, len(history_dict[\"loss\"]) + 1)\n", "\n", "plt.figure(figsize=(15, 5))\n", "plt.subplot(1, 3, 1)\n", "plt.plot(epochs, history_dict[\"loss\"], label=\"Train Loss\")\n", "plt.plot(epochs, history_dict[\"val_loss\"], label=\"Val Loss\")\n", "plt.title(\"Total Loss\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Loss\")\n", "plt.legend()\n", "\n", "plt.subplot(1, 3, 2)\n", "plt.plot(epochs, history_dict[\"mask_output_iou_coef\"], label=\"Train IOU (Mask)\")\n", "plt.plot(epochs, history_dict[\"val_mask_output_iou_coef\"], label=\"Val IOU (Mask)\")\n", "plt.title(\"Mask IOU\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"IOU\")\n", "plt.legend()\n", "\n", "plt.subplot(1, 3, 3)\n", "plt.plot(epochs, history_dict[\"boundary_output_iou_coef\"], label=\"Train IOU (Boundary)\")\n", "plt.plot(epochs, history_dict[\"val_boundary_output_iou_coef\"], label=\"Val IOU (Boundary)\")\n", "plt.title(\"Boundary IOU\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"IOU\")\n", "plt.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Animated Boundary Detection with U-Net\n", "\n", "## Objective\n", "\n", "This cell performs **frame-by-frame boundary detection** on a sequence of `.tif` images using a trained **U-Net** model. It visualizes the predicted boundaries as an **animation** over time, with each boundary overlaid on a preprocessed grayscale background.\n", "\n", "---\n", "\n", "## Workflow Overview\n", "\n", "### 1. **Preprocessing**\n", "- A static `background.bmp` is used to subtract background noise from each frame.\n", "- **Adaptive subtraction** is applied:\n", " - Accounts for both base intensity and relative background tolerance.\n", "- **CLAHE** (Contrast Limited Adaptive Histogram Equalization) enhances local contrast in the subtracted frame.\n", "\n", "### 2. **U-Net Prediction**\n", "- Each frame is resized to 256×256 for inference.\n", "- The U-Net model outputs a boundary probability map.\n", "- Post-processing includes:\n", " - Thresholding (`> 0.5`)\n", " - Morphological cleaning\n", " - Skeletonization using `skeletonize` and `thin`\n", " - Optional dilation to improve visibility\n", "\n", "### 3. **Overlay**\n", "- Predicted skeletons are overlaid in **red** on the contrast-enhanced grayscale frame.\n", "- Frames are stored in a list for animation.\n", "\n", "### 4. **Animation**\n", "- Frames are displayed as a **matplotlib animation** using `FuncAnimation`.\n", "- The result is rendered inline as an interactive JavaScript-based animation.\n", "\n", "---\n", "\n", "## Animation Features\n", "\n", "- Each frame shows:\n", " - Processed grayscale input (background-subtracted + contrast-enhanced)\n", " - Predicted **boundary skeletons** in red\n", "- Frame rate can be adjusted via `interval_ms` (default: 100 ms)\n", "\n", "---\n", "\n", "## Applications\n", "\n", "This animation pipeline is suitable for:\n", "\n", "- **Bubble tracking** in multiphase flows\n", "- **Dynamic segmentation** visualization in biological time-lapse imaging\n", "- **Debugging U-Net temporal predictions** in continuous video sequences\n", "\n", "---\n", "\n", "## Notes\n", "\n", "- This implementation assumes that all `.tif` frames and the background image are in the same directory.\n", "- Boundary skeletons are visualized, not filled contours, to emphasize **edge precision**.\n", "- `display(HTML(...))` ensures compatibility in **Jupyter Notebook environments**.\n", "\n", "---\n", "\n", "## Example Output\n", "\n", "> Below this markdown cell, the notebook will render a dynamic animation showing detected bubble boundaries evolving over time.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Animation size has reached 21220170 bytes, exceeding the limit of 20971520.0. If you're sure you want a larger animation embedded, set the animation.embed_limit rc parameter to a larger value (in MB). This and further frames will be dropped.\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import os\n", "import cv2\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "from skimage.morphology import skeletonize, thin\n", "from matplotlib import animation\n", "from IPython.display import HTML\n", "\n", "# ==== CONFIG ====\n", "folder = r\"C:\\Users\\Niepraschk\\Desktop\\Fuer Souman\\Research Project\\Github-Repository\\CFD-NN\\PHE\\Bubble detection algorithm\\U_Net\\Test datasets\\60 deg\\19_02_25-005_1600\"\n", "bg_name = \"background.bmp\"\n", "model_path = \"final_model_instance_boundary.keras\"\n", "IMG_H, IMG_W= 256, 256\n", "interval_ms = 100\n", "# ================\n", "\n", "# ==== HELPERS ====\n", "def adaptive_sub(frame, bg, base_tol=10, rel_tol=0.1):\n", " d = cv2.absdiff(frame, bg).astype(np.float32)\n", " tol = base_tol + rel_tol * bg.astype(np.float32)\n", " d[d < tol] = 0\n", " return np.clip(d,0,255).astype(np.uint8)\n", "\n", "def remove_small_components(binary_img, min_size=20):\n", " num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(binary_img, connectivity=8)\n", " cleaned = np.zeros_like(binary_img)\n", " for i in range(1, num_labels):\n", " if stats[i, cv2.CC_STAT_AREA] >= min_size:\n", " cleaned[labels == i] = 255\n", " return cleaned\n", "\n", "def process_mask(mask, size):\n", " mask = cv2.resize(mask, size, interpolation=cv2.INTER_NEAREST)\n", " mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, None)\n", " mask = remove_small_components(mask, min_size=20)\n", " skel = skeletonize(mask.astype(bool)).astype(np.uint8) * 255\n", " skel = thin(skel.astype(bool)).astype(np.uint8) * 255\n", " kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))\n", " skel_dil = cv2.dilate(skel, kernel, iterations=1) # Optional: can skip for 1px lines\n", " return skel_dil\n", "# =================\n", "\n", "# Load background\n", "bg_gray = cv2.imread(os.path.join(folder, bg_name), cv2.IMREAD_GRAYSCALE)\n", "if bg_gray is None:\n", " raise FileNotFoundError(f\"Cannot load background at {bg_name}\")\n", "\n", "# Load model\n", "model = tf.keras.models.load_model(\n", " model_path,\n", " custom_objects={\n", " \"iou_coef\": lambda y_true, y_pred: y_pred,\n", " \"bce_dice_loss\": lambda y_true, y_pred: y_pred\n", " }\n", ")\n", "\n", "# Gather frames\n", "file_list = sorted([f for f in os.listdir(folder) if f.lower().endswith(\".tif\")])\n", "if not file_list:\n", " raise RuntimeError(\"No .tif files found in folder\")\n", "\n", "frames = []\n", "for fname in file_list:\n", " raw_bgr = cv2.imread(os.path.join(folder, fname), cv2.IMREAD_COLOR)\n", " raw_rgb = cv2.cvtColor(raw_bgr, cv2.COLOR_BGR2RGB)\n", " raw_gray = cv2.cvtColor(raw_bgr, cv2.COLOR_BGR2GRAY)\n", " h, w = raw_gray.shape\n", "\n", " # Background subtraction + CLAHE\n", " proc = adaptive_sub(raw_gray, bg_gray)\n", " proc = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(12,12)).apply(proc)\n", "\n", " # Predict\n", " inp_raw = cv2.resize(raw_rgb, (IMG_W, IMG_W)).astype(np.float32) / 255.0\n", " pred = model.predict(inp_raw[None, ...], verbose=0)\n", " boundary = pred[1][0, ..., 0] if isinstance(pred, list) else pred[0][0, ..., 1]\n", "\n", " # Threshold, process, skeletonize\n", " mask256 = (boundary > 0.5).astype(np.uint8)\n", " skel_dil = process_mask(mask256, size=(w, h))\n", "\n", " # Overlay on gray\n", " overlay = cv2.cvtColor(proc, cv2.COLOR_GRAY2BGR)\n", " overlay[skel_dil > 0] = (0, 0, 255)\n", " panel = cv2.cvtColor(overlay, cv2.COLOR_BGR2RGB)\n", " frames.append(panel)\n", "\n", "# Animation\n", "def make_anim(frames, interval=100):\n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " ax.axis(\"off\")\n", " im = ax.imshow(frames[0])\n", " def update(i):\n", " im.set_array(frames[i])\n", " return [im]\n", " return animation.FuncAnimation(fig, update, frames=len(frames), interval=interval, blit=True)\n", "\n", "ani = make_anim(frames, interval=interval_ms)\n", "display(HTML(ani.to_jshtml()))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Boundary Refinement with Zoom-In Prediction\n", "\n", "## Objective\n", "\n", "This cell demonstrates a **coarse-to-fine segmentation strategy** using a trained U-Net model. It was tested to ascertain whether focusing on each bubble individually or yooming in provides any improvement in prediction. The processes involved are:\n", "\n", "1. Running a **coarse full-frame prediction**.\n", "2. Identifying the **largest object** in the boundary mask.\n", "3. **Zooming in** on the object region and applying **high-resolution prediction**.\n", "4. **Remapping** the refined prediction back to the original image space for comparison.\n", "\n", "---\n", "\n", "## Pipeline Overview\n", "\n", "### 🔹 Step 1: Full-Image Inference\n", "- The full RGB image is resized to 256×256 and passed through the model.\n", "- Model outputs:\n", " - `mask_pred`: segmentation mask\n", " - `boundary_pred`: boundary probability map\n", "- Thresholded and resized back to the original resolution.\n", "- **Refinement**:\n", " - Large connected components are re-predicted at full scale.\n", " - Morphological gradient is used to fill weak boundaries.\n", "\n", "### 🔹 Step 2: Largest Component Isolation\n", "- From the coarse boundary map, the **largest connected region** is extracted.\n", "- This is used to define a zoom-in region.\n", "\n", "### 🔹 Step 3: Zoom-In Re-Prediction\n", "- The crop of the largest region is resized to 256×256 and passed through the same model.\n", "- The predicted boundary map is thresholded and resized back to the crop's original size.\n", "\n", "### 🔹 Step 4: Remapping and Overlay\n", "- The refined boundary from the crop is **pasted** back into the original full-size image.\n", "- Both coarse and refined boundaries are visualized with **`cv2.drawContours()`**.\n", "\n", "---\n", "\n", "## Visual Comparison\n", "\n", "Two side-by-side plots are shown:\n", "- **Left**: Full-frame boundary prediction (coarse + refinement).\n", "- **Right**: Zoom-in prediction on the largest component, remapped to the original space.\n", "\n", "This comparison helps evaluate:\n", "- Whether the zoom-in step captures **finer boundary details**.\n", "- The effectiveness of hierarchical refinement strategies.\n", "\n", "---\n", "\n", "## Notes\n", "\n", "- `AREA_THRESH` determines which connected components are considered for refinement.\n", "- The fallback gradient ensures **boundary completeness** when the model misses regions.\n", "- This approach is ideal when full-resolution processing is computationally expensive.\n", "- This approach does not give any improvement in prediction\n", "\n" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import os\n", "import cv2\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "\n", "# ────────────────────────────────────────────────\n", "# 0) Settings & Model Load\n", "# ────────────────────────────────────────────────\n", "TEST_FOLDER = r\"C:\\Users\\Niepraschk\\Desktop\\Fuer Souman\\Research Project\\Github-Repository\\CFD-NN\\PHE\\Bubble detection algorithm\\U_Net\\Test datasets\\60 deg\\19_02_25-005_1600\"\n", "MODEL_PATH = \"final_model_instance_boundary.keras\"\n", "IMG_H, IMG_W = 256, 256\n", "AREA_THRESH = 5000\n", "CROP_BATCH = 8\n", "\n", "model = tf.keras.models.load_model(\n", " MODEL_PATH,\n", " custom_objects={\n", " \"iou_coef\": lambda y_true, y_pred: y_pred,\n", " \"bce_dice_loss\": lambda y_true, y_pred: y_pred\n", " }\n", ")\n", "\n", "# ────────────────────────────────────────────────\n", "# 1) Define Refinement & Processing\n", "# ────────────────────────────────────────────────\n", "def refine_and_complete_boundary(img, mask, bnd):\n", " # zoom-in re-predict\n", " n_lab, labels = cv2.connectedComponents(mask.astype(np.uint8), connectivity=8)\n", " refined = bnd.copy()\n", " crops, bboxes = [], []\n", " for lab in range(1, n_lab):\n", " area = (labels==lab).sum()\n", " if area < AREA_THRESH: continue\n", " ys, xs = np.where(labels==lab)\n", " y0,y1,x0,x1 = ys.min(), ys.max(), xs.min(), xs.max()\n", " patch = img[y0:y1+1, x0:x1+1]\n", " crops.append(cv2.resize(patch, (IMG_W, IMG_H)))\n", " bboxes.append((y0,y1,x0,x1))\n", " if crops:\n", " arr = np.stack(crops,0).astype(np.float32)/255.0\n", " _, b_preds = model.predict(arr, batch_size=CROP_BATCH, verbose=0)\n", " for (y0,y1,x0,x1), bp in zip(bboxes, b_preds):\n", " small = (bp[:,:,0] > 0.5).astype(np.uint8)\n", " up = cv2.resize(small, (x1-x0+1, y1-y0+1),\n", " interpolation=cv2.INTER_NEAREST)\n", " refined[y0:y1+1, x0:x1+1] = up\n", "\n", " # constrained fallback gradient\n", " kern = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))\n", " grad = cv2.morphologyEx(mask.astype(np.uint8),\n", " cv2.MORPH_GRADIENT,\n", " kern)\n", " mask_dil = cv2.dilate(mask.astype(np.uint8),\n", " kern,\n", " iterations=1)\n", " constrained_grad = cv2.bitwise_and(grad, mask_dil)\n", " refined = np.bitwise_or(refined, constrained_grad)\n", "\n", " return refined\n", "\n", "def process_image_rgb(img_rgb):\n", " H, W = img_rgb.shape[:2]\n", " small = cv2.resize(img_rgb, (IMG_W, IMG_H))\n", " inp = small.astype(np.float32)/255.0\n", " m_pred, b_pred = model.predict(inp[None,...], verbose=0)\n", " m_bin = (m_pred[0,:,:,0] > 0.5).astype(np.uint8)\n", " b_bin = (b_pred[0,:,:,0] > 0.5).astype(np.uint8)\n", " mask_full = cv2.resize(m_bin, (W,H), interpolation=cv2.INTER_NEAREST)\n", " bnd_full = cv2.resize(b_bin, (W,H), interpolation=cv2.INTER_NEAREST)\n", " b_final = refine_and_complete_boundary(img_rgb, mask_full, bnd_full)\n", "\n", " cnts,_ = cv2.findContours(b_final*255, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n", " out = img_rgb.copy()\n", " cv2.drawContours(out, cnts, -1, (255,0,0), 2)\n", " return out, b_final\n", "\n", "# ────────────────────────────────────────────────\n", "# 2) Load specific image\n", "# ────────────────────────────────────────────────\n", "fname = \"Bild000281.tif\"\n", "path = os.path.join(TEST_FOLDER, fname)\n", "orig_bgr = cv2.imread(path)\n", "orig_rgb = cv2.cvtColor(orig_bgr, cv2.COLOR_BGR2RGB)\n", "old_overlay, old_bnd = process_image_rgb(orig_rgb)\n", "\n", "# ────────────────────────────────────────────────\n", "# 3) Extract largest boundary component\n", "# ────────────────────────────────────────────────\n", "n_lab, labels = cv2.connectedComponents(old_bnd.astype(np.uint8), connectivity=8)\n", "max_area, best = 0, 0\n", "for lab in range(1, n_lab):\n", " area = (labels==lab).sum()\n", " if area > max_area:\n", " max_area, best = area, lab\n", "ys, xs = np.where(labels==best)\n", "y0,y1,x0,x1 = ys.min(), ys.max(), xs.min(), xs.max()\n", "masked_rgb = np.zeros_like(orig_rgb)\n", "masked_rgb[y0:y1+1, x0:x1+1] = orig_rgb[y0:y1+1, x0:x1+1]\n", "\n", "# ────────────────────────────────────────────────\n", "# 4) New overlay on masked region\n", "# ────────────────────────────────────────────────\n", "new_overlay, _ = process_image_rgb(masked_rgb)\n", "\n", "# ────────────────────────────────────────────────\n", "# 5) Show side by side\n", "# ────────────────────────────────────────────────\n", "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(14,6))\n", "ax1.imshow(old_overlay); ax1.set_title(f\"Old Overlay\\n{fname}\"); ax1.axis(\"off\")\n", "ax2.imshow(new_overlay); ax2.set_title(\"New Overlay on Largest Region\"); ax2.axis(\"off\")\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import os\n", "import cv2\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "\n", "# ────────────────────────────────────────────────\n", "# 0) Settings & Model Load\n", "# ────────────────────────────────────────────────\n", "TEST_FOLDER = r\"C:\\Users\\Niepraschk\\Desktop\\Fuer Souman\\Research Project\\Github-Repository\\CFD-NN\\PHE\\Bubble detection algorithm\\U_Net\\Test datasets\\60 deg\\19_02_25-005_1600\"\n", "MODEL_PATH = \"final_model_instance_boundary.keras\"\n", "IMG_H, IMG_W = 256, 256\n", "AREA_THRESH = 5000\n", "CROP_BATCH = 8\n", "\n", "model = tf.keras.models.load_model(\n", " MODEL_PATH,\n", " custom_objects={\n", " \"iou_coef\": lambda y_true, y_pred: y_pred,\n", " \"bce_dice_loss\": lambda y_true, y_pred: y_pred\n", " }\n", ")\n", "\n", "# ────────────────────────────────────────────────\n", "# 1) Full‐image processing (coarse + refine)\n", "# ────────────────────────────────────────────────\n", "def refine_and_complete_boundary(img, mask, bnd):\n", " # zoom-in refine on all large components\n", " n_lab, labels = cv2.connectedComponents(mask.astype(np.uint8), connectivity=8)\n", " refined = bnd.copy()\n", " crops, bboxes = [], []\n", " for lab in range(1, n_lab):\n", " area = (labels == lab).sum()\n", " if area < AREA_THRESH: continue\n", " ys, xs = np.where(labels == lab)\n", " y0,y1,x0,x1 = ys.min(), ys.max(), xs.min(), xs.max()\n", " patch = img[y0:y1+1, x0:x1+1]\n", " crops.append(cv2.resize(patch, (IMG_W, IMG_H)))\n", " bboxes.append((y0,y1,x0,x1))\n", " if crops:\n", " arr = np.stack(crops,0).astype(np.float32)/255.0\n", " _, b_preds = model.predict(arr, batch_size=CROP_BATCH, verbose=0)\n", " for (y0,y1,x0,x1), bp in zip(bboxes, b_preds):\n", " small = (bp[:,:,0] > 0.5).astype(np.uint8)\n", " up = cv2.resize(small, (x1-x0+1, y1-y0+1),\n", " interpolation=cv2.INTER_NEAREST)\n", " refined[y0:y1+1, x0:x1+1] = up\n", "\n", " # constrained fallback gradient (no dilation)\n", " kern = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))\n", " grad = cv2.morphologyEx(mask.astype(np.uint8),\n", " cv2.MORPH_GRADIENT,\n", " kern)\n", " constrained = cv2.bitwise_and(grad, mask.astype(np.uint8))\n", " refined = np.bitwise_or(refined, constrained)\n", " return refined\n", "\n", "def process_image_rgb(img_rgb):\n", " H,W = img_rgb.shape[:2]\n", " # coarse predict\n", " small = cv2.resize(img_rgb, (IMG_W, IMG_H))\n", " inp = small.astype(np.float32)/255.0\n", " m_pred, b_pred = model.predict(inp[None,...], verbose=0)\n", " m_bin = (m_pred[0,:,:,0] > 0.5).astype(np.uint8)\n", " b_bin = (b_pred[0,:,:,0] > 0.5).astype(np.uint8)\n", " mask_full = cv2.resize(m_bin, (W,H), interpolation=cv2.INTER_NEAREST)\n", " bnd_full = cv2.resize(b_bin, (W,H), interpolation=cv2.INTER_NEAREST)\n", " # refine\n", " b_final = refine_and_complete_boundary(img_rgb, mask_full, bnd_full)\n", " # draw\n", " cnts,_ = cv2.findContours(b_final*255, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n", " out = img_rgb.copy()\n", " cv2.drawContours(out, cnts, -1, (255,0,0), 2)\n", " return out, b_final\n", "\n", "# ────────────────────────────────────────────────\n", "# 2) Load specific image & full‐frame overlay\n", "# ────────────────────────────────────────────────\n", "fname = \"Bild000281.tif\"\n", "path = os.path.join(TEST_FOLDER, fname)\n", "orig_bgr = cv2.imread(path)\n", "orig_rgb = cv2.cvtColor(orig_bgr, cv2.COLOR_BGR2RGB)\n", "old_overlay, old_bnd = process_image_rgb(orig_rgb)\n", "\n", "# ────────────────────────────────────────────────\n", "# 3) Find largest connected component\n", "# ────────────────────────────────────────────────\n", "n_lab, labels = cv2.connectedComponents(old_bnd.astype(np.uint8), connectivity=8)\n", "max_area, best = 0, 0\n", "for lab in range(1, n_lab):\n", " area = (labels==lab).sum()\n", " if area > max_area:\n", " max_area, best = area, lab\n", "ys, xs = np.where(labels==best)\n", "y0,y1,x0,x1 = ys.min(), ys.max(), xs.min(), xs.max()\n", "\n", "# ────────────────────────────────────────────────\n", "# 4) Zoom‐in on that box, predict at 256×256, & remap\n", "# ────────────────────────────────────────────────\n", "# Crop & resize to network input\n", "crop = orig_rgb[y0:y1+1, x0:x1+1]\n", "crop_resized = cv2.resize(crop, (IMG_W, IMG_H),\n", " interpolation=cv2.INTER_LINEAR)\n", "\n", "# Model inference\n", "inp = crop_resized.astype(np.float32) / 255.0\n", "m2, b2 = model.predict(inp[None,...], verbose=0)\n", "\n", "# Threshold\n", "m2_bin = (m2[0,:,:,0] > 0.3).astype(np.uint8)\n", "b2_bin = (b2[0,:,:,0] > 0.3).astype(np.uint8)\n", "\n", "# Resize back to original crop size\n", "h_crop = y1 - y0 + 1\n", "w_crop = x1 - x0 + 1\n", "bnd_crop_full = cv2.resize(b2_bin, (w_crop, h_crop),\n", " interpolation=cv2.INTER_NEAREST)\n", "\n", "# Place into full‐size boundary map\n", "H, W = orig_rgb.shape[:2]\n", "b_final2 = np.zeros((H, W), dtype=np.uint8)\n", "b_final2[y0:y1+1, x0:x1+1] = bnd_crop_full\n", "\n", "# Draw new overlay\n", "new_overlay = orig_rgb.copy()\n", "cnts2,_ = cv2.findContours(b_final2*255, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n", "cv2.drawContours(new_overlay, cnts2, -1, (255,0,0), 2)\n", "\n", "# ────────────────────────────────────────────────\n", "# 5) Show results\n", "# ────────────────────────────────────────────────\n", "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(14,6))\n", "ax1.imshow(old_overlay)\n", "ax1.set_title(f\"Full‐frame Overlay\\n{fname}\")\n", "ax1.axis(\"off\")\n", "ax2.imshow(new_overlay)\n", "ax2.set_title(\"Zoom-in 256×256 → Predict → Remap\")\n", "ax2.axis(\"off\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 2 }