object_detection_using_vision_transformer.md

Object detection with Vision Transformers

Author: Karan V. Dave
Date created: 2022/03/27
Last modified: 2023/11/20
Description: A simple Keras implementation of object detection using Vision Transformers.

View in Colab • GitHub source

---

Introduction

The article Vision Transformer (ViT) architecture by Alexey Dosovitskiy et al. demonstrates that a pure transformer applied directly to sequences of image patches can perform well on object detection tasks.

In this Keras example, we implement an object detection ViT and we train it on the Caltech 101 dataset to detect an airplane in the given image.

---

Imports and setup

import os

os.environ["KERAS_BACKEND"] = "jax"  # @param ["tensorflow", "jax", "torch"]


import numpy as np
import keras
from keras import layers
from keras import ops
import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
import scipy.io
import shutil

---

Prepare dataset

We use the Caltech 101 Dataset.

# Path to images and annotations
path_images = "./101_ObjectCategories/airplanes/"
path_annot = "./Annotations/Airplanes_Side_2/"

path_to_downloaded_file = keras.utils.get_file(
    fname="caltech_101_zipped",
    origin="https://data.caltech.edu/records/mzrjq-6wc02/files/caltech-101.zip",
    extract=True,
    archive_format="zip",  # downloaded file format
    cache_dir="/",  # cache and extract in current directory
)
download_base_dir = os.path.dirname(path_to_downloaded_file)

# Extracting tar files found inside main zip file
shutil.unpack_archive(
    os.path.join(download_base_dir, "caltech-101", "101_ObjectCategories.tar.gz"), "."
)
shutil.unpack_archive(
    os.path.join(download_base_dir, "caltech-101", "Annotations.tar"), "."
)

# list of paths to images and annotations
image_paths = [
    f for f in os.listdir(path_images) if os.path.isfile(os.path.join(path_images, f))
]
annot_paths = [
    f for f in os.listdir(path_annot) if os.path.isfile(os.path.join(path_annot, f))
]

image_paths.sort()
annot_paths.sort()

image_size = 224  # resize input images to this size

images, targets = [], []

# loop over the annotations and images, preprocess them and store in lists
for i in range(0, len(annot_paths)):
    # Access bounding box coordinates
    annot = scipy.io.loadmat(path_annot + annot_paths[i])["box_coord"][0]

    top_left_x, top_left_y = annot[2], annot[0]
    bottom_right_x, bottom_right_y = annot[3], annot[1]

    image = keras.utils.load_img(
        path_images + image_paths[i],
    )
    (w, h) = image.size[:2]

    # resize images
    image = image.resize((image_size, image_size))

    # convert image to array and append to list
    images.append(keras.utils.img_to_array(image))

    # apply relative scaling to bounding boxes as per given image and append to list
    targets.append(
        (
            float(top_left_x) / w,
            float(top_left_y) / h,
            float(bottom_right_x) / w,
            float(bottom_right_y) / h,
        )
    )

# Convert the list to numpy array, split to train and test dataset
(x_train), (y_train) = (
    np.asarray(images[: int(len(images) * 0.8)]),
    np.asarray(targets[: int(len(targets) * 0.8)]),
)
(x_test), (y_test) = (
    np.asarray(images[int(len(images) * 0.8) :]),
    np.asarray(targets[int(len(targets) * 0.8) :]),
)

---

Implement multilayer-perceptron (MLP)

We use the code from the Keras example Image classification with Vision Transformer as a reference.

def mlp(x, hidden_units, dropout_rate):
    for units in hidden_units:
        x = layers.Dense(units, activation=keras.activations.gelu)(x)
        x = layers.Dropout(dropout_rate)(x)
    return x

---

Implement the patch creation layer

class Patches(layers.Layer):
    def __init__(self, patch_size):
        super().__init__()
        self.patch_size = patch_size

    def call(self, images):
        input_shape = ops.shape(images)
        batch_size = input_shape[0]
        height = input_shape[1]
        width = input_shape[2]
        channels = input_shape[3]
        num_patches_h = height // self.patch_size
        num_patches_w = width // self.patch_size
        patches = keras.ops.image.extract_patches(images, size=self.patch_size)
        patches = ops.reshape(
            patches,
            (
                batch_size,
                num_patches_h * num_patches_w,
                self.patch_size * self.patch_size * channels,
            ),
        )
        return patches

    def get_config(self):
        config = super().get_config()
        config.update({"patch_size": self.patch_size})
        return config

---

Display patches for an input image

patch_size = 32  # Size of the patches to be extracted from the input images

plt.figure(figsize=(4, 4))
plt.imshow(x_train[0].astype("uint8"))
plt.axis("off")

patches = Patches(patch_size)(np.expand_dims(x_train[0], axis=0))
print(f"Image size: {image_size} X {image_size}")
print(f"Patch size: {patch_size} X {patch_size}")
print(f"{patches.shape[1]} patches per image \n{patches.shape[-1]} elements per patch")


n = int(np.sqrt(patches.shape[1]))
plt.figure(figsize=(4, 4))
for i, patch in enumerate(patches[0]):
    ax = plt.subplot(n, n, i + 1)
    patch_img = ops.reshape(patch, (patch_size, patch_size, 3))
    plt.imshow(ops.convert_to_numpy(patch_img).astype("uint8"))
    plt.axis("off")

``` Image size: 224 X 224 Patch size: 32 X 32 49 patches per image 3072 elements per patch

</div>
    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_11_1.png)
    



    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_11_2.png)
    


---
## Implement the patch encoding layer

The `PatchEncoder` layer linearly transforms a patch by projecting it into a
vector of size `projection_dim`. It also adds a learnable position
embedding to the projected vector.


```python

class PatchEncoder(layers.Layer):
    def __init__(self, num_patches, projection_dim):
        super().__init__()
        self.num_patches = num_patches
        self.projection = layers.Dense(units=projection_dim)
        self.position_embedding = layers.Embedding(
            input_dim=num_patches, output_dim=projection_dim
        )

    # Override function to avoid error while saving model
    def get_config(self):
        config = super().get_config().copy()
        config.update(
            {
                "input_shape": input_shape,
                "patch_size": patch_size,
                "num_patches": num_patches,
                "projection_dim": projection_dim,
                "num_heads": num_heads,
                "transformer_units": transformer_units,
                "transformer_layers": transformer_layers,
                "mlp_head_units": mlp_head_units,
            }
        )
        return config

    def call(self, patch):
        positions = ops.expand_dims(
            ops.arange(start=0, stop=self.num_patches, step=1), axis=0
        )
        projected_patches = self.projection(patch)
        encoded = projected_patches + self.position_embedding(positions)
        return encoded

---

Build the ViT model

The ViT model has multiple Transformer blocks. The MultiHeadAttention layer is used for self-attention, applied to the sequence of image patches. The encoded patches (skip connection) and self-attention layer outputs are normalized and fed into a multilayer perceptron (MLP). The model outputs four dimensions representing the bounding box coordinates of an object.

def create_vit_object_detector(
    input_shape,
    patch_size,
    num_patches,
    projection_dim,
    num_heads,
    transformer_units,
    transformer_layers,
    mlp_head_units,
):
    inputs = keras.Input(shape=input_shape)
    # Create patches
    patches = Patches(patch_size)(inputs)
    # Encode patches
    encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)

    # Create multiple layers of the Transformer block.
    for _ in range(transformer_layers):
        # Layer normalization 1.
        x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
        # Create a multi-head attention layer.
        attention_output = layers.MultiHeadAttention(
            num_heads=num_heads, key_dim=projection_dim, dropout=0.1
        )(x1, x1)
        # Skip connection 1.
        x2 = layers.Add()([attention_output, encoded_patches])
        # Layer normalization 2.
        x3 = layers.LayerNormalization(epsilon=1e-6)(x2)
        # MLP
        x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)
        # Skip connection 2.
        encoded_patches = layers.Add()([x3, x2])

    # Create a [batch_size, projection_dim] tensor.
    representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
    representation = layers.Flatten()(representation)
    representation = layers.Dropout(0.3)(representation)
    # Add MLP.
    features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.3)

    bounding_box = layers.Dense(4)(
        features
    )  # Final four neurons that output bounding box

    # return Keras model.
    return keras.Model(inputs=inputs, outputs=bounding_box)

---

Run the experiment

def run_experiment(model, learning_rate, weight_decay, batch_size, num_epochs):
    optimizer = keras.optimizers.AdamW(
        learning_rate=learning_rate, weight_decay=weight_decay
    )

    # Compile model.
    model.compile(optimizer=optimizer, loss=keras.losses.MeanSquaredError())

    checkpoint_filepath = "vit_object_detector.weights.h5"
    checkpoint_callback = keras.callbacks.ModelCheckpoint(
        checkpoint_filepath,
        monitor="val_loss",
        save_best_only=True,
        save_weights_only=True,
    )

    history = model.fit(
        x=x_train,
        y=y_train,
        batch_size=batch_size,
        epochs=num_epochs,
        validation_split=0.1,
        callbacks=[
            checkpoint_callback,
            keras.callbacks.EarlyStopping(monitor="val_loss", patience=10),
        ],
    )

    return history


input_shape = (image_size, image_size, 3)  # input image shape
learning_rate = 0.001
weight_decay = 0.0001
batch_size = 32
num_epochs = 100
num_patches = (image_size // patch_size) ** 2
projection_dim = 64
num_heads = 4
# Size of the transformer layers
transformer_units = [
    projection_dim * 2,
    projection_dim,
]
transformer_layers = 4
mlp_head_units = [2048, 1024, 512, 64, 32]  # Size of the dense layers


history = []
num_patches = (image_size // patch_size) ** 2

vit_object_detector = create_vit_object_detector(
    input_shape,
    patch_size,
    num_patches,
    projection_dim,
    num_heads,
    transformer_units,
    transformer_layers,
    mlp_head_units,
)

# Train model
history = run_experiment(
    vit_object_detector, learning_rate, weight_decay, batch_size, num_epochs
)


def plot_history(item):
    plt.plot(history.history[item], label=item)
    plt.plot(history.history["val_" + item], label="val_" + item)
    plt.xlabel("Epochs")
    plt.ylabel(item)
    plt.title("Train and Validation {} Over Epochs".format(item), fontsize=14)
    plt.legend()
    plt.grid()
    plt.show()


plot_history("loss")

``` Epoch 1/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 9s 109ms/step - loss: 1.2097 - val_loss: 0.3468 Epoch 2/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 0.4260 - val_loss: 0.3102 Epoch 3/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 0.3268 - val_loss: 0.2727 Epoch 4/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 0.2815 - val_loss: 0.2391 Epoch 5/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 0.2290 - val_loss: 0.1735 Epoch 6/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - loss: 0.1870 - val_loss: 0.1055 Epoch 7/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 0.1401 - val_loss: 0.0610 Epoch 8/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 0.1122 - val_loss: 0.0274 Epoch 9/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0924 - val_loss: 0.0296 Epoch 10/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - loss: 0.0765 - val_loss: 0.0139 Epoch 11/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 0.0597 - val_loss: 0.0111 Epoch 12/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 0.0540 - val_loss: 0.0101 Epoch 13/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - loss: 0.0432 - val_loss: 0.0053 Epoch 14/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - loss: 0.0380 - val_loss: 0.0052 Epoch 15/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 0.0334 - val_loss: 0.0030 Epoch 16/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - loss: 0.0283 - val_loss: 0.0021 Epoch 17/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - loss: 0.0228 - val_loss: 0.0012 Epoch 18/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0244 - val_loss: 0.0017 Epoch 19/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0195 - val_loss: 0.0016 Epoch 20/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0189 - val_loss: 0.0020 Epoch 21/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0191 - val_loss: 0.0019 Epoch 22/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0174 - val_loss: 0.0016 Epoch 23/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0157 - val_loss: 0.0020 Epoch 24/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0157 - val_loss: 0.0015 Epoch 25/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0139 - val_loss: 0.0023 Epoch 26/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0130 - val_loss: 0.0017 Epoch 27/100 18/18 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.0157 - val_loss: 0.0014

</div>
    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_17_1.png)
    


---
## Evaluate the model


```python
import matplotlib.patches as patches

# Saves the model in current path
vit_object_detector.save("vit_object_detector.keras")


# To calculate IoU (intersection over union, given two bounding boxes)
def bounding_box_intersection_over_union(box_predicted, box_truth):
    # get (x, y) coordinates of intersection of bounding boxes
    top_x_intersect = max(box_predicted[0], box_truth[0])
    top_y_intersect = max(box_predicted[1], box_truth[1])
    bottom_x_intersect = min(box_predicted[2], box_truth[2])
    bottom_y_intersect = min(box_predicted[3], box_truth[3])

    # calculate area of the intersection bb (bounding box)
    intersection_area = max(0, bottom_x_intersect - top_x_intersect + 1) * max(
        0, bottom_y_intersect - top_y_intersect + 1
    )

    # calculate area of the prediction bb and ground-truth bb
    box_predicted_area = (box_predicted[2] - box_predicted[0] + 1) * (
        box_predicted[3] - box_predicted[1] + 1
    )
    box_truth_area = (box_truth[2] - box_truth[0] + 1) * (
        box_truth[3] - box_truth[1] + 1
    )

    # calculate intersection over union by taking intersection
    # area and dividing it by the sum of predicted bb and ground truth
    # bb areas subtracted by  the interesection area

    # return ioU
    return intersection_area / float(
        box_predicted_area + box_truth_area - intersection_area
    )


i, mean_iou = 0, 0

# Compare results for 10 images in the test set
for input_image in x_test[:10]:
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 15))
    im = input_image

    # Display the image
    ax1.imshow(im.astype("uint8"))
    ax2.imshow(im.astype("uint8"))

    input_image = cv2.resize(
        input_image, (image_size, image_size), interpolation=cv2.INTER_AREA
    )
    input_image = np.expand_dims(input_image, axis=0)
    preds = vit_object_detector.predict(input_image)[0]

    (h, w) = (im).shape[0:2]

    top_left_x, top_left_y = int(preds[0] * w), int(preds[1] * h)

    bottom_right_x, bottom_right_y = int(preds[2] * w), int(preds[3] * h)

    box_predicted = [top_left_x, top_left_y, bottom_right_x, bottom_right_y]
    # Create the bounding box
    rect = patches.Rectangle(
        (top_left_x, top_left_y),
        bottom_right_x - top_left_x,
        bottom_right_y - top_left_y,
        facecolor="none",
        edgecolor="red",
        linewidth=1,
    )
    # Add the bounding box to the image
    ax1.add_patch(rect)
    ax1.set_xlabel(
        "Predicted: "
        + str(top_left_x)
        + ", "
        + str(top_left_y)
        + ", "
        + str(bottom_right_x)
        + ", "
        + str(bottom_right_y)
    )

    top_left_x, top_left_y = int(y_test[i][0] * w), int(y_test[i][1] * h)

    bottom_right_x, bottom_right_y = int(y_test[i][2] * w), int(y_test[i][3] * h)

    box_truth = top_left_x, top_left_y, bottom_right_x, bottom_right_y

    mean_iou += bounding_box_intersection_over_union(box_predicted, box_truth)
    # Create the bounding box
    rect = patches.Rectangle(
        (top_left_x, top_left_y),
        bottom_right_x - top_left_x,
        bottom_right_y - top_left_y,
        facecolor="none",
        edgecolor="red",
        linewidth=1,
    )
    # Add the bounding box to the image
    ax2.add_patch(rect)
    ax2.set_xlabel(
        "Target: "
        + str(top_left_x)
        + ", "
        + str(top_left_y)
        + ", "
        + str(bottom_right_x)
        + ", "
        + str(bottom_right_y)
        + "\n"
        + "IoU"
        + str(bounding_box_intersection_over_union(box_predicted, box_truth))
    )
    i = i + 1

print("mean_iou: " + str(mean_iou / len(x_test[:10])))
plt.show()

``` 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 1s/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step mean_iou: 0.9092338486331416

</div>
    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_19_1.png)
    



    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_19_2.png)
    



    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_19_3.png)
    



    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_19_4.png)
    



    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_19_5.png)
    



    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_19_6.png)
    



    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_19_7.png)
    



    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_19_8.png)
    



    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_19_9.png)
    



    
![png](/img/examples/vision/object_detection_using_vision_transformer/object_detection_using_vision_transformer_19_10.png)
    


This example demonstrates that a pure Transformer can be trained
to predict the bounding boxes of an object in a given image,
thus extending the use of Transformers to object detection tasks.
The model can be improved further by tuning hyper-parameters and pre-training.