Image classification from scratch

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class='k-content'> <h1 id="image-classification-from-scratch">Image classification from scratch</h1> Author: <a href="https://twitter.com/fchollet">fchollet</a> Date created: 2020/04/27 Last modified: 2023/11/09 Description: Training an image classifier from scratch on the Kaggle Cats vs Dogs 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/image_classification_from_scratch.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/image_classification_from_scratch.py">GitHub source</a> <hr /> <h2 id="introduction">Introduction</h2> This example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset. We use the <code>image_dataset_from_directory</code> utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation. <hr /> <h2 id="setup">Setup</h2> <div class="codehilite"><pre><code>import os import numpy as np import keras from keras import layers from tensorflow import data as tf_data import matplotlib.pyplot as plt </code></pre></div> <hr /> <h2 id="load-the-data-the-cats-vs-dogs-dataset">Load the data: the Cats vs Dogs dataset</h2> <h3 id="raw-data-download">Raw data download</h3> First, let's download the 786M ZIP archive of the raw data: <div class="codehilite"><pre><code>!curl -O https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip </code></pre></div> <div class="codehilite"><pre><code>!unzip -q kagglecatsanddogs_5340.zip !ls </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 786M 100 786M 0 0 11.1M 0 0:01:10 0:01:10 --:--:-- 11.8M CDLA-Permissive-2.0.pdf kagglecatsanddogs_5340.zip PetImages 'readme[1].txt' image_classification_from_scratch.ipynb </code></pre></div> </div> Now we have a <code>PetImages</code> folder which contain two subfolders, <code>Cat</code> and <code>Dog</code>. Each subfolder contains image files for each category. <div class="codehilite"><pre><code>!ls PetImages </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Cat Dog </code></pre></div> </div> <h3 id="filter-out-corrupted-images">Filter out corrupted images</h3> When working with lots of real-world image data, corrupted images are a common occurence. Let's filter out badly-encoded images that do not feature the string "JFIF" in their header. <div class="codehilite"><pre><code>num_skipped = 0 for folder_name in ("Cat", "Dog"): folder_path = os.path.join("PetImages", folder_name) for fname in os.listdir(folder_path): fpath = os.path.join(folder_path, fname) try: fobj = open(fpath, "rb") is_jfif = b"JFIF" in fobj.peek(10) finally: fobj.close() if not is_jfif: num_skipped += 1 # Delete corrupted image os.remove(fpath) print(f"Deleted {num_skipped} images.") </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Deleted 1590 images. </code></pre></div> </div> <hr /> <h2 id="generate-a-dataset">Generate a <code>Dataset</code></h2> <div class="codehilite"><pre><code>image_size = (180, 180) batch_size = 128 train_ds, val_ds = keras.utils.image_dataset_from_directory( "PetImages", validation_split=0.2, subset="both", seed=1337, image_size=image_size, batch_size=batch_size, ) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Found 23410 files belonging to 2 classes. Using 18728 files for training. Using 4682 files for validation. </code></pre></div> </div> <hr /> <h2 id="visualize-the-data">Visualize the data</h2> Here are the first 9 images in the training dataset. <div class="codehilite"><pre><code>plt.figure(figsize=(10, 10)) for images, labels in train_ds.take(1): for i in range(9): ax = plt.subplot(3, 3, i + 1) plt.imshow(np.array(images[i]).astype("uint8")) plt.title(int(labels[i])) plt.axis("off") </code></pre></div> <img alt="png" src="/img/examples/vision/image_classification_from_scratch/image_classification_from_scratch_14_1.png" /> <hr /> <h2 id="using-image-data-augmentation">Using image data augmentation</h2> When you don't have a large image dataset, it's a good practice to artificially introduce sample diversity by applying random yet realistic transformations to the training images, such as random horizontal flipping or small random rotations. This helps expose the model to different aspects of the training data while slowing down overfitting. <div class="codehilite"><pre><code>data_augmentation_layers = [ layers.RandomFlip("horizontal"), layers.RandomRotation(0.1), ] def data_augmentation(images): for layer in data_augmentation_layers: images = layer(images) return images </code></pre></div> Let's visualize what the augmented samples look like, by applying <code>data_augmentation</code> repeatedly to the first few images in the dataset: <div class="codehilite"><pre><code>plt.figure(figsize=(10, 10)) for images, _ in train_ds.take(1): for i in range(9): augmented_images = data_augmentation(images) ax = plt.subplot(3, 3, i + 1) plt.imshow(np.array(augmented_images[0]).astype("uint8")) plt.axis("off") </code></pre></div> <img alt="png" src="/img/examples/vision/image_classification_from_scratch/image_classification_from_scratch_18_1.png" /> <hr /> <h2 id="standardizing-the-data">Standardizing the data</h2> Our image are already in a standard size (180x180), as they are being yielded as contiguous <code>float32</code> batches by our dataset. However, their RGB channel values are in the <code>[0, 255]</code> range. This is not ideal for a neural network; in general you should seek to make your input values small. Here, we will standardize values to be in the <code>[0, 1]</code> by using a <code>Rescaling</code> layer at the start of our model. <hr /> <h2 id="two-options-to-preprocess-the-data">Two options to preprocess the data</h2> There are two ways you could be using the <code>data_augmentation</code> preprocessor: Option 1: Make it part of the model, like this: <div class="codehilite"><pre><code>inputs = keras.Input(shape=input_shape) x = data_augmentation(inputs) x = layers.Rescaling(1./255)(x) ... # Rest of the model </code></pre></div> With this option, your data augmentation will happen on device, synchronously with the rest of the model execution, meaning that it will benefit from GPU acceleration. Note that data augmentation is inactive at test time, so the input samples will only be augmented during <code>fit()</code>, not when calling <code>evaluate()</code> or <code>predict()</code>. If you're training on GPU, this may be a good option. Option 2: apply it to the dataset, so as to obtain a dataset that yields batches of augmented images, like this: <div class="codehilite"><pre><code>augmented_train_ds = train_ds.map( lambda x, y: (data_augmentation(x, training=True), y)) </code></pre></div> With this option, your data augmentation will happen on CPU, asynchronously, and will be buffered before going into the model. If you're training on CPU, this is the better option, since it makes data augmentation asynchronous and non-blocking. In our case, we'll go with the second option. If you're not sure which one to pick, this second option (asynchronous preprocessing) is always a solid choice. <hr /> <h2 id="configure-the-dataset-for-performance">Configure the dataset for performance</h2> Let's apply data augmentation to our training dataset, and let's make sure to use buffered prefetching so we can yield data from disk without having I/O becoming blocking: <div class="codehilite"><pre><code># Apply `data_augmentation` to the training images. train_ds = train_ds.map( lambda img, label: (data_augmentation(img), label), num_parallel_calls=tf_data.AUTOTUNE, ) # Prefetching samples in GPU memory helps maximize GPU utilization. train_ds = train_ds.prefetch(tf_data.AUTOTUNE) val_ds = val_ds.prefetch(tf_data.AUTOTUNE) </code></pre></div> <hr /> <h2 id="build-a-model">Build a model</h2> We'll build a small version of the Xception network. We haven't particularly tried to optimize the architecture; if you want to do a systematic search for the best model configuration, consider using <a href="https://github.com/keras-team/keras-tuner">KerasTuner</a>. Note that: <ul> <li>We start the model with the <code>data_augmentation</code> preprocessor, followed by a <code>Rescaling</code> layer.</li> <li>We include a <code>Dropout</code> layer before the final classification layer.</li> </ul> <div class="codehilite"><pre><code>def make_model(input_shape, num_classes): inputs = keras.Input(shape=input_shape) # Entry block x = layers.Rescaling(1.0 / 255)(inputs) x = layers.Conv2D(128, 3, strides=2, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.Activation("relu")(x) previous_block_activation = x # Set aside residual for size in [256, 512, 728]: x = layers.Activation("relu")(x) x = layers.SeparableConv2D(size, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.Activation("relu")(x) x = layers.SeparableConv2D(size, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.MaxPooling2D(3, strides=2, padding="same")(x) # Project residual residual = layers.Conv2D(size, 1, strides=2, padding="same")( previous_block_activation ) x = layers.add([x, residual]) # Add back residual previous_block_activation = x # Set aside next residual x = layers.SeparableConv2D(1024, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.Activation("relu")(x) x = layers.GlobalAveragePooling2D()(x) if num_classes == 2: units = 1 else: units = num_classes x = layers.Dropout(0.25)(x) # We specify activation=None so as to return logits outputs = layers.Dense(units, activation=None)(x) return keras.Model(inputs, outputs) model = make_model(input_shape=image_size + (3,), num_classes=2) keras.utils.plot_model(model, show_shapes=True) </code></pre></div> <img alt="png" src="/img/examples/vision/image_classification_from_scratch/image_classification_from_scratch_24_0.png" /> <hr /> <h2 id="train-the-model">Train the model</h2> <div class="codehilite"><pre><code>epochs = 25 callbacks = [ keras.callbacks.ModelCheckpoint("save_at_{epoch}.keras"), ] model.compile( optimizer=keras.optimizers.Adam(3e-4), loss=keras.losses.BinaryCrossentropy(from_logits=True), metrics=[keras.metrics.BinaryAccuracy(name="acc")], ) model.fit( train_ds, epochs=epochs, callbacks=callbacks, validation_data=val_ds, ) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 1/25 ... Epoch 25/25 147/147 ━━━━━━━━━━━━━━━━━━━━ 53s 354ms/step - acc: 0.9638 - loss: 0.0903 - val_acc: 0.9382 - val_loss: 0.1542 <keras.src.callbacks.history.History at 0x7f41003c24a0> </code></pre></div> </div> We get to >90% validation accuracy after training for 25 epochs on the full dataset (in practice, you can train for 50+ epochs before validation performance starts degrading). <hr /> <h2 id="run-inference-on-new-data">Run inference on new data</h2> Note that data augmentation and dropout are inactive at inference time. <div class="codehilite"><pre><code>img = keras.utils.load_img("PetImages/Cat/6779.jpg", target_size=image_size) plt.imshow(img) img_array = keras.utils.img_to_array(img) img_array = keras.ops.expand_dims(img_array, 0) # Create batch axis predictions = model.predict(img_array) score = float(keras.ops.sigmoid(predictions[0][0])) print(f"This image is {100 * (1 - score):.2f}% cat and {100 * score:.2f}% dog.") </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code> 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step This image is 94.30% cat and 5.70% dog. </code></pre></div> </div> <img alt="png" src="/img/examples/vision/image_classification_from_scratch/image_classification_from_scratch_29_1.png" /> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#image-classification-from-scratch'>Image classification from scratch</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#introduction'>Introduction</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#setup'>Setup</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#load-the-data-the-cats-vs-dogs-dataset'>Load the data: the Cats vs Dogs dataset</a> </div> <div class='k-outline-depth-3'> <a href='#raw-data-download'>Raw data download</a> </div> <div class='k-outline-depth-3'> <a href='#filter-out-corrupted-images'>Filter out corrupted images</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#generate-a-dataset'>Generate a <code>Dataset</code></a> </div> <div class='k-outline-depth-2'> ◆ <a href='#visualize-the-data'>Visualize the data</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#using-image-data-augmentation'>Using image data augmentation</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#standardizing-the-data'>Standardizing the data</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#two-options-to-preprocess-the-data'>Two options to preprocess the data</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#configure-the-dataset-for-performance'>Configure the dataset for performance</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#build-a-model'>Build a model</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='#run-inference-on-new-data'>Run inference on new data</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>

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Image classification from scratch