Exploring ResNet50: An In-Depth Look at the Model Architecture and Code Implementation (2024)

ResNet50 is a deep convolutional neural network (CNN) architecture that was developed by Microsoft Research in 2015. It is a variant of the popular ResNet architecture, which stands for “Residual Network.” The “50” in the name refers to the number of layers in the network, which is 50 layers deep.

ResNet50 is a powerful image classification model that can be trained on large datasets and achieve state-of-the-art results. One of its key innovations is the use of residual connections, which allow the network to learn a set of residual functions that map the input to the desired output. These residual connections enable the network to learn much deeper architectures than was previously possible, without suffering from the problem of vanishing gradients.

The architecture of ResNet50 is divided into four main parts: the convolutional layers, the identity block, the convolutional block, and the fully connected layers. The convolutional layers are responsible for extracting features from the input image, while the identity block and convolutional block are responsible for processing and transforming these features. Finally, the fully connected layers are used to make the final classification.

The convolutional layers in ResNet50 consist of several convolutional layers followed by batch normalization and ReLU activation. These layers are responsible for extracting features from the input image, such as edges, textures, and shapes. The convolutional layers are followed by max pooling layers, which reduce the spatial dimensions of the feature maps while preserving the most important features.

The identity block and convolutional block are the key building blocks of ResNet50. The identity block is a simple block that passes the input through a series of convolutional layers and adds the input back to the output. This allows the network to learn residual functions that map the input to the desired output. The convolutional block is similar to the identity block, but with the addition of a 1x1 convolutional layer that is used to reduce the number of filters before the 3x3 convolutional layer.

The final part of ResNet50 is the fully connected layers. These layers are responsible for making the final classification. The output of the final fully connected layer is fed into a softmax activation function to produce the final class probabilities.

ResNet50 has been trained on large datasets and achieves state-of-the-art results on several benchmarks. It has been trained on the ImageNet dataset, which contains over 14 million images and 1000 classes. On this dataset, ResNet50 achieved an error rate of 22.85% which is on par with human performance, which is an error rate of 5.1%.

Here is an example of how to use ResNet50 for transfer learning with images in Python using the Keras library:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, Sequential
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import pathlib
dataset = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
directory = tf.keras.utils.get_file('flower_photos', origin=dataset, untar=True)
data_directory = pathlib.Path(directory)
# Define the image size and batch size
img_height, img_width = 180, 180
batch_size = 32
# Create the training and validation datasets
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
 data_directory,
 validation_split=0.2,
 subset="training",
 seed=123,
 image_size=(img_height, img_width),
 batch_size=batch_size
)
validation_ds = tf.keras.preprocessing.image_dataset_from_directory(
 data_directory,
 validation_split=0.2,
 subset="validation",
 seed=123,
 image_size=(img_height, img_width),
 batch_size=batch_size
)
# Plot some sample images from the dataset
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
 for i in range(6):
 ax = plt.subplot(3, 3, i + 1)
 plt.imshow(images[i].numpy().astype("uint8"))
 plt.title(classnames[labels[i]])
 plt.axis("off")
# Create the ResNet50 model and set the layers to be non-trainable
resnet_model = Sequential()
pretrained_model = tf.keras.applications.ResNet50(include_top=False,
 input_shape=(img_height, img_width, 3),
 pooling='avg',
 weights='imagenet')
for layer in pretrained_model.layers:
 layer.trainable = False
resnet_model.add(pretrained_model)
# Add fully connected layers for classification
resnet_model.add(layers.Flatten())
resnet_model.add(layers.Dense(512, activation='relu'))
resnet_model.add(layers.Dense(5, activation='softmax'))
# Compile and train the model
resnet_model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
history = resnet_model.fit(train_ds, validation_data=validation_ds, epochs=10)

Continuing with Evaluation and model inference:

# Evaluate the ResNet-50 model
fig1 = plt.gcf()
plt.plot(history.history['accuracy'])
plt.plot(history.history['validation_accuracy'])
plt.axis(ymin=0.4, ymax=1)
plt.grid()
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()
# Model Inference
# Preprocess the sample image
import cv2
image = cv2.imread(str(roses[0]))
image_resized = cv2.resize(image, (img_height, img_width))
image = np.expand_dims(image_resized, axis=0)
# Make predictions
image_pred = resnet_model.predict(image)
# Produce a human-readable output label
image_output_class = class_names[np.argmax(image_pred)]
print("The predicted class is", image_output_class)

How it solved the problem of vanishing gradients:

Skip connections, also known as residual connections, are a key feature of the ResNet50 architecture. They are used to allow the network to learn deeper architectures without suffering from the problem of vanishing gradients.

Vanishing gradients is a problem that occurs when training deep neural networks, where the gradients of the parameters in the deeper layers become very small, making it difficult for those layers to learn and improve. This problem becomes more pronounced as the network becomes deeper.

Skip connections address this problem by allowing the information to flow directly from the input to the output of the network, bypassing one or more layers. This allows the network to learn residual functions that map the input to the desired output, rather than having to learn the entire mapping from scratch.

In ResNet50, skip connections are used in the identity block and convolutional block. The identity block passes the input through a series of convolutional layers and adds the input back to the output, while the convolutional block uses a 1x1 convolutional layer to reduce the number of filters before the 3x3 convolutional layer and then adds the input back to the output.

The use of skip connections in ResNet50 allows the network to learn deeper architectures while still being able to train effectively and prevent vanishing gradients.

In summary, ResNet50 is a cutting-edge deep convolutional neural network architecture that was developed by Microsoft Research in 2015. It is a variant of the popular ResNet architecture and comprises of 50 layers that enable it to learn much deeper architectures than previously possible without encountering the problem of vanishing gradients. The architecture of ResNet50 is divided into four main parts: the convolutional layers, the identity block, the convolutional block, and the fully connected layers. The convolutional layers are responsible for extracting features from the input image, the identity block and convolutional block process and transform these features, and the fully connected layers make the final classification. ResNet50 has been trained on the large ImageNet dataset, achieving an error rate on par with human performance, making it a powerful model for various image classification tasks such as object detection, facial recognition and medical image analysis. Additionally, it has also been used as a feature extractor for other tasks, such as object detection and semantic segmentation.

“ResNet50, with its deep residual networks, opened the door for the training of even deeper architectures and helped push the boundaries of what was possible in computer vision.” — Yann LeCun, Director of AI Research at Facebook