» Code examples / Computer Vision / Image classification from scratch

Image classification from scratch

Author: fchollet
Date created: 2020/04/27
Last modified: 2022/11/10
Description: Training an image classifier from scratch on the Kaggle Cats vs Dogs dataset.

View in Colab GitHub source


Introduction

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 image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation.


Setup

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

Load the data: the Cats vs Dogs dataset

Raw data download

First, let's download the 786M ZIP archive of the raw data:

!curl -O https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip
!unzip -q kagglecatsanddogs_5340.zip
!ls
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  786M  100  786M    0     0   182M      0  0:00:04  0:00:04 --:--:--  195M

 CDLA-Permissive-2.0.pdf           kagglecatsanddogs_5340.zip
 PetImages                'readme[1].txt'
 image_classification_from_scratch.ipynb

Now we have a PetImages folder which contain two subfolders, Cat and Dog. Each subfolder contains image files for each category.

!ls PetImages
Cat  Dog

Filter out corrupted images

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.

import os

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 = tf.compat.as_bytes("JFIF") in fobj.peek(10)
        finally:
            fobj.close()

        if not is_jfif:
            num_skipped += 1
            # Delete corrupted image
            os.remove(fpath)

print("Deleted %d images" % num_skipped)
Deleted 1590 images

Generate a Dataset

image_size = (180, 180)
batch_size = 128

train_ds, val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    "PetImages",
    validation_split=0.2,
    subset="both",
    seed=1337,
    image_size=image_size,
    batch_size=batch_size,
)
Found 23410 files belonging to 2 classes.
Using 18728 files for training.
Using 4682 files for validation.

Visualize the data

Here are the first 9 images in the training dataset. As you can see, label 1 is "dog" and label 0 is "cat".

import matplotlib.pyplot as plt

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(images[i].numpy().astype("uint8"))
        plt.title(int(labels[i]))
        plt.axis("off")
Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9

png


Using image data augmentation

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.

data_augmentation = keras.Sequential(
    [
        layers.RandomFlip("horizontal"),
        layers.RandomRotation(0.1),
    ]
)

Let's visualize what the augmented samples look like, by applying data_augmentation repeatedly to the first image in the dataset:

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(augmented_images[0].numpy().astype("uint8"))
        plt.axis("off")

png


Standardizing the data

Our image are already in a standard size (180x180), as they are being yielded as contiguous float32 batches by our dataset. However, their RGB channel values are in the [0, 255] 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 [0, 1] by using a Rescaling layer at the start of our model.


Two options to preprocess the data

There are two ways you could be using the data_augmentation preprocessor:

Option 1: Make it part of the model, like this:

inputs = keras.Input(shape=input_shape)
x = data_augmentation(inputs)
x = layers.Rescaling(1./255)(x)
...  # Rest of the model

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 fit(), not when calling evaluate() or predict().

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:

augmented_train_ds = train_ds.map(
    lambda x, y: (data_augmentation(x, training=True), y))

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.


Configure the dataset for performance

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:

# 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)

Build a model

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 KerasTuner.

Note that:

  • We start the model with the data_augmentation preprocessor, followed by a Rescaling layer.
  • We include a Dropout layer before the final classification layer.
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:
        activation = "sigmoid"
        units = 1
    else:
        activation = "softmax"
        units = num_classes

    x = layers.Dropout(0.5)(x)
    outputs = layers.Dense(units, activation=activation)(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)

png


Train the model

epochs = 25

callbacks = [
    keras.callbacks.ModelCheckpoint("save_at_{epoch}.keras"),
]
model.compile(
    optimizer=keras.optimizers.Adam(1e-3),
    loss="binary_crossentropy",
    metrics=["accuracy"],
)
model.fit(
    train_ds,
    epochs=epochs,
    callbacks=callbacks,
    validation_data=val_ds,
)
Epoch 1/25
147/147 [==============================] - 116s 746ms/step - loss: 0.6531 - accuracy: 0.6416 - val_loss: 0.7669 - val_accuracy: 0.4957
Epoch 2/25
147/147 [==============================] - 109s 737ms/step - loss: 0.5026 - accuracy: 0.7559 - val_loss: 1.3825 - val_accuracy: 0.4957
Epoch 3/25
147/147 [==============================] - 109s 738ms/step - loss: 0.3928 - accuracy: 0.8243 - val_loss: 1.6816 - val_accuracy: 0.4957
Epoch 4/25
147/147 [==============================] - 109s 736ms/step - loss: 0.3307 - accuracy: 0.8588 - val_loss: 0.5025 - val_accuracy: 0.7520
Epoch 5/25
147/147 [==============================] - 109s 734ms/step - loss: 0.2758 - accuracy: 0.8860 - val_loss: 0.3462 - val_accuracy: 0.8545
Epoch 6/25
147/147 [==============================] - 109s 735ms/step - loss: 0.2357 - accuracy: 0.9023 - val_loss: 0.2712 - val_accuracy: 0.8825
Epoch 7/25
147/147 [==============================] - 109s 734ms/step - loss: 0.2011 - accuracy: 0.9201 - val_loss: 0.2131 - val_accuracy: 0.9135
Epoch 8/25
147/147 [==============================] - 109s 735ms/step - loss: 0.1787 - accuracy: 0.9275 - val_loss: 0.1969 - val_accuracy: 0.9227
Epoch 9/25
147/147 [==============================] - 109s 734ms/step - loss: 0.1650 - accuracy: 0.9321 - val_loss: 0.2306 - val_accuracy: 0.9178
Epoch 10/25
147/147 [==============================] - 109s 734ms/step - loss: 0.1474 - accuracy: 0.9408 - val_loss: 0.2430 - val_accuracy: 0.9107
Epoch 11/25
147/147 [==============================] - 109s 735ms/step - loss: 0.1352 - accuracy: 0.9461 - val_loss: 0.2783 - val_accuracy: 0.8768
Epoch 12/25
147/147 [==============================] - 109s 734ms/step - loss: 0.1291 - accuracy: 0.9474 - val_loss: 0.4632 - val_accuracy: 0.8419
Epoch 13/25
147/147 [==============================] - 109s 735ms/step - loss: 0.1208 - accuracy: 0.9521 - val_loss: 0.3907 - val_accuracy: 0.8456
Epoch 14/25
147/147 [==============================] - 110s 739ms/step - loss: 0.1162 - accuracy: 0.9553 - val_loss: 0.1503 - val_accuracy: 0.9417
Epoch 15/25
147/147 [==============================] - 109s 735ms/step - loss: 0.1037 - accuracy: 0.9598 - val_loss: 0.1484 - val_accuracy: 0.9406
Epoch 16/25
147/147 [==============================] - 109s 734ms/step - loss: 0.1018 - accuracy: 0.9605 - val_loss: 0.2480 - val_accuracy: 0.9054
Epoch 17/25
147/147 [==============================] - 109s 739ms/step - loss: 0.0949 - accuracy: 0.9629 - val_loss: 0.1585 - val_accuracy: 0.9378
Epoch 18/25
147/147 [==============================] - 109s 736ms/step - loss: 0.0941 - accuracy: 0.9622 - val_loss: 0.1452 - val_accuracy: 0.9432
Epoch 19/25
147/147 [==============================] - 109s 734ms/step - loss: 0.0862 - accuracy: 0.9668 - val_loss: 0.2644 - val_accuracy: 0.8904
Epoch 20/25
147/147 [==============================] - 109s 734ms/step - loss: 0.0889 - accuracy: 0.9656 - val_loss: 0.2335 - val_accuracy: 0.9182
Epoch 21/25
147/147 [==============================] - 109s 735ms/step - loss: 0.0792 - accuracy: 0.9687 - val_loss: 0.5037 - val_accuracy: 0.8751
Epoch 22/25
147/147 [==============================] - 109s 734ms/step - loss: 0.0651 - accuracy: 0.9737 - val_loss: 0.1103 - val_accuracy: 0.9551
Epoch 23/25
147/147 [==============================] - 109s 735ms/step - loss: 0.0641 - accuracy: 0.9751 - val_loss: 0.1846 - val_accuracy: 0.9299
Epoch 24/25
147/147 [==============================] - 109s 735ms/step - loss: 0.0709 - accuracy: 0.9735 - val_loss: 0.1151 - val_accuracy: 0.9575
Epoch 25/25
147/147 [==============================] - 109s 737ms/step - loss: 0.0612 - accuracy: 0.9768 - val_loss: 0.1259 - val_accuracy: 0.9510

<keras.callbacks.History at 0x7fd3941c87b8>

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).


Run inference on new data

img = keras.preprocessing.image.load_img(
    "PetImages/Cat/6779.jpg", target_size=image_size
)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)  # Create batch axis

predictions = model.predict(img_array)
score = float(predictions[0])
print(f"This image is {100 * (1 - score):.2f}% cat and {100 * score:.2f}% dog.")
1/1 [==============================] - 0s 446ms/step
This image is 85.28% cat and 14.72% dog.