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class='k-main-inner' id='k-main-id'> <div class='k-location-slug'> ► <a href='/examples/'>Code examples</a> / <a href='/examples/vision/'>Computer Vision</a> / Image segmentation with a U-Net-like architecture </div> <div class='k-content'> <h1 id="image-segmentation-with-a-unetlike-architecture">Image segmentation with a U-Net-like architecture</h1> Author: <a href="https://twitter.com/fchollet">fchollet</a> Date created: 2019/03/20 Last modified: 2020/04/20 Description: Image segmentation model trained from scratch on the Oxford Pets dataset. <div class='example_version_banner keras_3'>ⓘ This example uses Keras 3</div> <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> <a href="https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/oxford_pets_image_segmentation.ipynb">View in Colab</a> •<img class="k-inline-icon" src="https://github.com/favicon.ico"/> <a href="https://github.com/keras-team/keras-io/blob/master/examples/vision/oxford_pets_image_segmentation.py">GitHub source</a> <hr /> <h2 id="download-the-data">Download the data</h2> <div class="codehilite"><pre><code>!!wget https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz !!wget https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz ! !curl -O https://thor.robots.ox.ac.uk/datasets/pets/images.tar.gz !curl -O https://thor.robots.ox.ac.uk/datasets/pets/annotations.tar.gz ! !tar -xf images.tar.gz !tar -xf annotations.tar.gz </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code> % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 755M 100 755M 0 0 21.3M 0 0:00:35 0:00:35 --:--:-- 22.2M % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 18.2M 100 18.2M 0 0 7977k 0 0:00:02 0:00:02 --:--:-- 7974k </code></pre></div> </div> <hr /> <h2 id="prepare-paths-of-input-images-and-target-segmentation-masks">Prepare paths of input images and target segmentation masks</h2> <div class="codehilite"><pre><code>import os input_dir = "images/" target_dir = "annotations/trimaps/" img_size = (160, 160) num_classes = 3 batch_size = 32 input_img_paths = sorted( [ os.path.join(input_dir, fname) for fname in os.listdir(input_dir) if fname.endswith(".jpg") ] ) target_img_paths = sorted( [ os.path.join(target_dir, fname) for fname in os.listdir(target_dir) if fname.endswith(".png") and not fname.startswith(".") ] ) print("Number of samples:", len(input_img_paths)) for input_path, target_path in zip(input_img_paths[:10], target_img_paths[:10]): print(input_path, "|", target_path) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Number of samples: 7390 images/Abyssinian_1.jpg | annotations/trimaps/Abyssinian_1.png images/Abyssinian_10.jpg | annotations/trimaps/Abyssinian_10.png images/Abyssinian_100.jpg | annotations/trimaps/Abyssinian_100.png images/Abyssinian_101.jpg | annotations/trimaps/Abyssinian_101.png images/Abyssinian_102.jpg | annotations/trimaps/Abyssinian_102.png images/Abyssinian_103.jpg | annotations/trimaps/Abyssinian_103.png images/Abyssinian_104.jpg | annotations/trimaps/Abyssinian_104.png images/Abyssinian_105.jpg | annotations/trimaps/Abyssinian_105.png images/Abyssinian_106.jpg | annotations/trimaps/Abyssinian_106.png images/Abyssinian_107.jpg | annotations/trimaps/Abyssinian_107.png </code></pre></div> </div> <hr /> <h2 id="what-does-one-input-image-and-corresponding-segmentation-mask-look-like">What does one input image and corresponding segmentation mask look like?</h2> <div class="codehilite"><pre><code>from IPython.display import Image, display from keras.utils import load_img from PIL import ImageOps # Display input image #7 display(Image(filename=input_img_paths[9])) # Display auto-contrast version of corresponding target (per-pixel categories) img = ImageOps.autocontrast(load_img(target_img_paths[9])) display(img) </code></pre></div> <img alt="jpeg" src="/img/examples/vision/oxford_pets_image_segmentation/oxford_pets_image_segmentation_6_0.jpg" /> <img alt="png" src="/img/examples/vision/oxford_pets_image_segmentation/oxford_pets_image_segmentation_6_1.png" /> <hr /> <h2 id="prepare-dataset-to-load-amp-vectorize-batches-of-data">Prepare dataset to load & vectorize batches of data</h2> <div class="codehilite"><pre><code>import keras import numpy as np from tensorflow import data as tf_data from tensorflow import image as tf_image from tensorflow import io as tf_io def get_dataset( batch_size, img_size, input_img_paths, target_img_paths, max_dataset_len=None, ): """Returns a TF Dataset.""" def load_img_masks(input_img_path, target_img_path): input_img = tf_io.read_file(input_img_path) input_img = tf_io.decode_png(input_img, channels=3) input_img = tf_image.resize(input_img, img_size) input_img = tf_image.convert_image_dtype(input_img, "float32") target_img = tf_io.read_file(target_img_path) target_img = tf_io.decode_png(target_img, channels=1) target_img = tf_image.resize(target_img, img_size, method="nearest") target_img = tf_image.convert_image_dtype(target_img, "uint8") # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2: target_img -= 1 return input_img, target_img # For faster debugging, limit the size of data if max_dataset_len: input_img_paths = input_img_paths[:max_dataset_len] target_img_paths = target_img_paths[:max_dataset_len] dataset = tf_data.Dataset.from_tensor_slices((input_img_paths, target_img_paths)) dataset = dataset.map(load_img_masks, num_parallel_calls=tf_data.AUTOTUNE) return dataset.batch(batch_size) </code></pre></div> <hr /> <h2 id="prepare-unet-xceptionstyle-model">Prepare U-Net Xception-style model</h2> <div class="codehilite"><pre><code>from keras import layers def get_model(img_size, num_classes): inputs = keras.Input(shape=img_size + (3,)) ### [First half of the network: downsampling inputs] ### # Entry block x = layers.Conv2D(32, 3, strides=2, padding="same")(inputs) x = layers.BatchNormalization()(x) x = layers.Activation("relu")(x) previous_block_activation = x # Set aside residual # Blocks 1, 2, 3 are identical apart from the feature depth. for filters in [64, 128, 256]: x = layers.Activation("relu")(x) x = layers.SeparableConv2D(filters, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.Activation("relu")(x) x = layers.SeparableConv2D(filters, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.MaxPooling2D(3, strides=2, padding="same")(x) # Project residual residual = layers.Conv2D(filters, 1, strides=2, padding="same")( previous_block_activation ) x = layers.add([x, residual]) # Add back residual previous_block_activation = x # Set aside next residual ### [Second half of the network: upsampling inputs] ### for filters in [256, 128, 64, 32]: x = layers.Activation("relu")(x) x = layers.Conv2DTranspose(filters, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.Activation("relu")(x) x = layers.Conv2DTranspose(filters, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.UpSampling2D(2)(x) # Project residual residual = layers.UpSampling2D(2)(previous_block_activation) residual = layers.Conv2D(filters, 1, padding="same")(residual) x = layers.add([x, residual]) # Add back residual previous_block_activation = x # Set aside next residual # Add a per-pixel classification layer outputs = layers.Conv2D(num_classes, 3, activation="softmax", padding="same")(x) # Define the model model = keras.Model(inputs, outputs) return model # Build model model = get_model(img_size, num_classes) model.summary() </code></pre></div> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">Model: "functional_1" </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ │ input_layer │ (None, 160, 160, │ 0 │ - │ │ (InputLayer) │ 3) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d (Conv2D) │ (None, 80, 80, │ 896 │ input_layer[0][0] │ │ │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalization │ (None, 80, 80, │ 128 │ conv2d[0][0] │ │ (BatchNormalizatio… │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation │ (None, 80, 80, │ 0 │ batch_normalization… │ │ (Activation) │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_1 │ (None, 80, 80, │ 0 │ activation[0][0] │ │ (Activation) │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ separable_conv2d │ (None, 80, 80, │ 2,400 │ activation_1[0][0] │ │ (SeparableConv2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 80, 80, │ 256 │ separable_conv2d[0]… │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_2 │ (None, 80, 80, │ 0 │ batch_normalization… │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ separable_conv2d_1 │ (None, 80, 80, │ 4,736 │ activation_2[0][0] │ │ (SeparableConv2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 80, 80, │ 256 │ separable_conv2d_1[… │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ max_pooling2d │ (None, 40, 40, │ 0 │ batch_normalization… │ │ (MaxPooling2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_1 (Conv2D) │ (None, 40, 40, │ 2,112 │ activation[0][0] │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ add (Add) │ (None, 40, 40, │ 0 │ max_pooling2d[0][0], │ │ │ 64) │ │ conv2d_1[0][0] │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_3 │ (None, 40, 40, │ 0 │ add[0][0] │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ separable_conv2d_2 │ (None, 40, 40, │ 8,896 │ activation_3[0][0] │ │ (SeparableConv2D) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 40, 40, │ 512 │ separable_conv2d_2[… │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_4 │ (None, 40, 40, │ 0 │ batch_normalization… │ │ (Activation) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ separable_conv2d_3 │ (None, 40, 40, │ 17,664 │ activation_4[0][0] │ │ (SeparableConv2D) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 40, 40, │ 512 │ separable_conv2d_3[… │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ max_pooling2d_1 │ (None, 20, 20, │ 0 │ batch_normalization… │ │ (MaxPooling2D) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_2 (Conv2D) │ (None, 20, 20, │ 8,320 │ add[0][0] │ │ │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ add_1 (Add) │ (None, 20, 20, │ 0 │ max_pooling2d_1[0][… │ │ │ 128) │ │ conv2d_2[0][0] │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_5 │ (None, 20, 20, │ 0 │ add_1[0][0] │ │ (Activation) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ separable_conv2d_4 │ (None, 20, 20, │ 34,176 │ activation_5[0][0] │ │ (SeparableConv2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 20, 20, │ 1,024 │ separable_conv2d_4[… │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_6 │ (None, 20, 20, │ 0 │ batch_normalization… │ │ (Activation) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ separable_conv2d_5 │ (None, 20, 20, │ 68,096 │ activation_6[0][0] │ │ (SeparableConv2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 20, 20, │ 1,024 │ separable_conv2d_5[… │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ max_pooling2d_2 │ (None, 10, 10, │ 0 │ batch_normalization… │ │ (MaxPooling2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_3 (Conv2D) │ (None, 10, 10, │ 33,024 │ add_1[0][0] │ │ │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ add_2 (Add) │ (None, 10, 10, │ 0 │ max_pooling2d_2[0][… │ │ │ 256) │ │ conv2d_3[0][0] │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_7 │ (None, 10, 10, │ 0 │ add_2[0][0] │ │ (Activation) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_transpose │ (None, 10, 10, │ 590,080 │ activation_7[0][0] │ │ (Conv2DTranspose) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 10, 10, │ 1,024 │ conv2d_transpose[0]… │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_8 │ (None, 10, 10, │ 0 │ batch_normalization… │ │ (Activation) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_transpose_1 │ (None, 10, 10, │ 590,080 │ activation_8[0][0] │ │ (Conv2DTranspose) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 10, 10, │ 1,024 │ conv2d_transpose_1[… │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ up_sampling2d_1 │ (None, 20, 20, │ 0 │ add_2[0][0] │ │ (UpSampling2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ up_sampling2d │ (None, 20, 20, │ 0 │ batch_normalization… │ │ (UpSampling2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_4 (Conv2D) │ (None, 20, 20, │ 65,792 │ up_sampling2d_1[0][… │ │ │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ add_3 (Add) │ (None, 20, 20, │ 0 │ up_sampling2d[0][0], │ │ │ 256) │ │ conv2d_4[0][0] │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_9 │ (None, 20, 20, │ 0 │ add_3[0][0] │ │ (Activation) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_transpose_2 │ (None, 20, 20, │ 295,040 │ activation_9[0][0] │ │ (Conv2DTranspose) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 20, 20, │ 512 │ conv2d_transpose_2[… │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_10 │ (None, 20, 20, │ 0 │ batch_normalization… │ │ (Activation) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_transpose_3 │ (None, 20, 20, │ 147,584 │ activation_10[0][0] │ │ (Conv2DTranspose) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 20, 20, │ 512 │ conv2d_transpose_3[… │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ up_sampling2d_3 │ (None, 40, 40, │ 0 │ add_3[0][0] │ │ (UpSampling2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ up_sampling2d_2 │ (None, 40, 40, │ 0 │ batch_normalization… │ │ (UpSampling2D) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_5 (Conv2D) │ (None, 40, 40, │ 32,896 │ up_sampling2d_3[0][… │ │ │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ add_4 (Add) │ (None, 40, 40, │ 0 │ up_sampling2d_2[0][… │ │ │ 128) │ │ conv2d_5[0][0] │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_11 │ (None, 40, 40, │ 0 │ add_4[0][0] │ │ (Activation) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_transpose_4 │ (None, 40, 40, │ 73,792 │ activation_11[0][0] │ │ (Conv2DTranspose) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 40, 40, │ 256 │ conv2d_transpose_4[… │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_12 │ (None, 40, 40, │ 0 │ batch_normalization… │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_transpose_5 │ (None, 40, 40, │ 36,928 │ activation_12[0][0] │ │ (Conv2DTranspose) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 40, 40, │ 256 │ conv2d_transpose_5[… │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ up_sampling2d_5 │ (None, 80, 80, │ 0 │ add_4[0][0] │ │ (UpSampling2D) │ 128) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ up_sampling2d_4 │ (None, 80, 80, │ 0 │ batch_normalization… │ │ (UpSampling2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_6 (Conv2D) │ (None, 80, 80, │ 8,256 │ up_sampling2d_5[0][… │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ add_5 (Add) │ (None, 80, 80, │ 0 │ up_sampling2d_4[0][… │ │ │ 64) │ │ conv2d_6[0][0] │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_13 │ (None, 80, 80, │ 0 │ add_5[0][0] │ │ (Activation) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_transpose_6 │ (None, 80, 80, │ 18,464 │ activation_13[0][0] │ │ (Conv2DTranspose) │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 80, 80, │ 128 │ conv2d_transpose_6[… │ │ (BatchNormalizatio… │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ activation_14 │ (None, 80, 80, │ 0 │ batch_normalization… │ │ (Activation) │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_transpose_7 │ (None, 80, 80, │ 9,248 │ activation_14[0][0] │ │ (Conv2DTranspose) │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ batch_normalizatio… │ (None, 80, 80, │ 128 │ conv2d_transpose_7[… │ │ (BatchNormalizatio… │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ up_sampling2d_7 │ (None, 160, 160, │ 0 │ add_5[0][0] │ │ (UpSampling2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ up_sampling2d_6 │ (None, 160, 160, │ 0 │ batch_normalization… │ │ (UpSampling2D) │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_7 (Conv2D) │ (None, 160, 160, │ 2,080 │ up_sampling2d_7[0][… │ │ │ 32) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ add_6 (Add) │ (None, 160, 160, │ 0 │ up_sampling2d_6[0][… │ │ │ 32) │ │ conv2d_7[0][0] │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ conv2d_8 (Conv2D) │ (None, 160, 160, │ 867 │ add_6[0][0] │ │ │ 3) │ │ │ └─────────────────────┴───────────────────┴─────────┴──────────────────────┘ </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Total params: 2,058,979 (7.85 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Trainable params: 2,055,203 (7.84 MB) </pre> <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> Non-trainable params: 3,776 (14.75 KB) </pre> <hr /> <h2 id="set-aside-a-validation-split">Set aside a validation split</h2> <div class="codehilite"><pre><code>import random # Split our img paths into a training and a validation set val_samples = 1000 random.Random(1337).shuffle(input_img_paths) random.Random(1337).shuffle(target_img_paths) train_input_img_paths = input_img_paths[:-val_samples] train_target_img_paths = target_img_paths[:-val_samples] val_input_img_paths = input_img_paths[-val_samples:] val_target_img_paths = target_img_paths[-val_samples:] # Instantiate dataset for each split # Limit input files in `max_dataset_len` for faster epoch training time. # Remove the `max_dataset_len` arg when running with full dataset. train_dataset = get_dataset( batch_size, img_size, train_input_img_paths, train_target_img_paths, max_dataset_len=1000, ) valid_dataset = get_dataset( batch_size, img_size, val_input_img_paths, val_target_img_paths ) </code></pre></div> <hr /> <h2 id="train-the-model">Train the model</h2> <div class="codehilite"><pre><code># Configure the model for training. # We use the "sparse" version of categorical_crossentropy # because our target data is integers. model.compile( optimizer=keras.optimizers.Adam(1e-4), loss="sparse_categorical_crossentropy" ) callbacks = [ keras.callbacks.ModelCheckpoint("oxford_segmentation.keras", save_best_only=True) ] # Train the model, doing validation at the end of each epoch. epochs = 50 model.fit( train_dataset, epochs=epochs, validation_data=valid_dataset, callbacks=callbacks, verbose=2, ) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 1/50 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1700414690.172044 2226172 device_compiler.h:187] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process. Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 62s - 2s/step - loss: 1.6363 - val_loss: 2.2226 Epoch 2/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 94ms/step - loss: 0.9223 - val_loss: 1.8273 Epoch 3/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 82ms/step - loss: 0.7894 - val_loss: 2.0044 Epoch 4/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 83ms/step - loss: 0.7174 - val_loss: 2.3480 Epoch 5/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 82ms/step - loss: 0.6695 - val_loss: 2.7528 Epoch 6/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 83ms/step - loss: 0.6325 - val_loss: 3.1453 Epoch 7/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 84ms/step - loss: 0.6012 - val_loss: 3.5611 Epoch 8/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 87ms/step - loss: 0.5730 - val_loss: 4.0003 Epoch 9/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 85ms/step - loss: 0.5466 - val_loss: 4.4798 Epoch 10/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 86ms/step - loss: 0.5210 - val_loss: 5.0245 Epoch 11/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 87ms/step - loss: 0.4958 - val_loss: 5.5950 Epoch 12/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 87ms/step - loss: 0.4706 - val_loss: 6.1534 Epoch 13/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 85ms/step - loss: 0.4453 - val_loss: 6.6107 Epoch 14/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 83ms/step - loss: 0.4202 - val_loss: 6.8010 Epoch 15/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 84ms/step - loss: 0.3956 - val_loss: 6.6751 Epoch 16/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 83ms/step - loss: 0.3721 - val_loss: 6.0800 Epoch 17/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 84ms/step - loss: 0.3506 - val_loss: 5.1820 Epoch 18/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 82ms/step - loss: 0.3329 - val_loss: 4.0350 Epoch 19/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 4s - 114ms/step - loss: 0.3216 - val_loss: 3.0513 Epoch 20/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 94ms/step - loss: 0.3595 - val_loss: 2.2567 Epoch 21/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 100ms/step - loss: 0.4417 - val_loss: 1.5873 Epoch 22/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 101ms/step - loss: 0.3531 - val_loss: 1.5798 Epoch 23/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 96ms/step - loss: 0.3353 - val_loss: 1.5525 Epoch 24/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 95ms/step - loss: 0.3392 - val_loss: 1.4625 Epoch 25/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 95ms/step - loss: 0.3596 - val_loss: 0.8867 Epoch 26/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 94ms/step - loss: 0.3528 - val_loss: 0.8021 Epoch 27/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 92ms/step - loss: 0.3237 - val_loss: 0.7986 Epoch 28/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 89ms/step - loss: 0.3198 - val_loss: 0.8533 Epoch 29/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 84ms/step - loss: 0.3272 - val_loss: 1.0588 Epoch 30/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 88ms/step - loss: 0.3164 - val_loss: 1.1889 Epoch 31/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 85ms/step - loss: 0.2987 - val_loss: 0.9518 Epoch 32/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 87ms/step - loss: 0.2749 - val_loss: 0.9011 Epoch 33/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 84ms/step - loss: 0.2595 - val_loss: 0.8872 Epoch 34/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 87ms/step - loss: 0.2552 - val_loss: 1.0221 Epoch 35/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 82ms/step - loss: 0.2628 - val_loss: 1.1553 Epoch 36/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 85ms/step - loss: 0.2788 - val_loss: 2.1549 Epoch 37/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 94ms/step - loss: 0.2870 - val_loss: 1.6282 Epoch 38/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 89ms/step - loss: 0.2702 - val_loss: 1.3201 Epoch 39/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 91ms/step - loss: 0.2569 - val_loss: 1.2364 Epoch 40/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 106ms/step - loss: 0.2523 - val_loss: 1.3673 Epoch 41/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 86ms/step - loss: 0.2570 - val_loss: 1.3999 Epoch 42/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 87ms/step - loss: 0.2680 - val_loss: 0.9976 Epoch 43/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 83ms/step - loss: 0.2558 - val_loss: 1.0209 Epoch 44/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 85ms/step - loss: 0.2403 - val_loss: 1.3271 Epoch 45/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 83ms/step - loss: 0.2414 - val_loss: 1.1993 Epoch 46/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 84ms/step - loss: 0.2516 - val_loss: 1.0532 Epoch 47/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 83ms/step - loss: 0.2695 - val_loss: 1.1183 Epoch 48/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 87ms/step - loss: 0.2555 - val_loss: 1.0432 Epoch 49/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 82ms/step - loss: 0.2290 - val_loss: 0.9444 Epoch 50/50 Corrupt JPEG data: 240 extraneous bytes before marker 0xd9 32/32 - 3s - 83ms/step - loss: 0.1994 - val_loss: 1.2182 <keras.src.callbacks.history.History at 0x7fe01842dab0> </code></pre></div> </div> <hr /> <h2 id="visualize-predictions">Visualize predictions</h2> <div class="codehilite"><pre><code># Generate predictions for all images in the validation set val_dataset = get_dataset( batch_size, img_size, val_input_img_paths, val_target_img_paths ) val_preds = model.predict(val_dataset) def display_mask(i): """Quick utility to display a model's prediction.""" mask = np.argmax(val_preds[i], axis=-1) mask = np.expand_dims(mask, axis=-1) img = ImageOps.autocontrast(keras.utils.array_to_img(mask)) display(img) # Display results for validation image #10 i = 10 # Display input image display(Image(filename=val_input_img_paths[i])) # Display ground-truth target mask img = ImageOps.autocontrast(load_img(val_target_img_paths[i])) display(img) # Display mask predicted by our model display_mask(i) # Note that the model only sees inputs at 150x150. </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code> 32/32 ━━━━━━━━━━━━━━━━━━━━ 5s 100ms/step </code></pre></div> </div> <img alt="jpeg" src="/img/examples/vision/oxford_pets_image_segmentation/oxford_pets_image_segmentation_16_1.jpg" /> <img alt="png" src="/img/examples/vision/oxford_pets_image_segmentation/oxford_pets_image_segmentation_16_2.png" /> <img alt="png" src="/img/examples/vision/oxford_pets_image_segmentation/oxford_pets_image_segmentation_16_3.png" /> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#image-segmentation-with-a-unetlike-architecture'>Image segmentation with a U-Net-like architecture</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#download-the-data'>Download the data</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#prepare-paths-of-input-images-and-target-segmentation-masks'>Prepare paths of input images and target segmentation masks</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#what-does-one-input-image-and-corresponding-segmentation-mask-look-like'>What does one input image and corresponding segmentation mask look like?</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#prepare-dataset-to-load-amp-vectorize-batches-of-data'>Prepare dataset to load & vectorize batches of data</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#prepare-unet-xceptionstyle-model'>Prepare U-Net Xception-style model</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#set-aside-a-validation-split'>Set aside a validation split</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#train-the-model'>Train the model</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#visualize-predictions'>Visualize predictions</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>