In this tutorial, we'll learn about ResNet model and how to use a pre-trained ResNet-50 model for image classification with PyTorch. We'll go through the steps of loading a pre-trained model, preprocessing image, and using the model to predict its class label, as well as displaying the results.The tutorial covers:
- Introduction to ResNet model
- Load a Pre-Trained ResNet-50 model
- Define Image Preprocessing
- Load ImageNet Class Labels
- Make a Prediction
- Conclusion
- Full code listing
Introduction to ResNet model
ResNet, short for Residual Network, is a deep convolutional neural network (CNN) architecture that addresses a key problem in very deep networks: the vanishing gradient problem, where gradients shrink as they’re back-propagated through layers, making it hard to train deeper networks effectively. ResNet enables the training of extremely deep networks by using residual connections, which allow gradients to flow more easily through the network.
Residual blocks
In traditional CNNs, layers are arranged in a sequence of convolutions, batch normalization, and activation functions. This setup can make training very deep networks difficult. In ResNet, a residual block adds a shortcut (or skip connection) that allows the input to jump over one or more layers. This shortcut helps the network learn a residual mapping instead of trying to learn the entire transformation.
The output of the residual block is calculated as:
where:
- is the input to the block
- is the transformation done by the convolutional layers in the block
The output combines the original input and the result from the convolutional layers.
Key Characteristics of ResNet
Deep Architecture: ResNet comes in various depths, such as ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152, with the latter capable of containing over 150 layers. This depth allows the model to capture intricate patterns and features in data.
Residual Connections: The hallmark of ResNet is its use of skip connections (or shortcuts) that bypass one or more layers. This allows the network to learn residual mappings, which helps in mitigating the vanishing gradient problem and enables effective training of very deep networks.
Bottleneck Design: In deeper variants like ResNet-50 and above, the architecture employs bottleneck blocks that consist of three convolutional layers (1x1, 3x3, and 1x1). This design reduces the number of parameters while maintaining performance.
Global Average Pooling: Instead of fully connected layers, ResNet often utilizes global average pooling, which significantly reduces the number of parameters and helps combat overfitting.
Limitations
The primary challenge is the increased computational requirements associated with very deep networks, which can lead to longer training times and higher resource consumption. Additionally, while residual connections alleviate the vanishing gradient problem, they do not entirely eliminate it, and training very deep networks can still be complex.
Loading a Pre-Trained ResNet-50 Model
Before starting, make sure you have the following Python libraries installed:
torch
(PyTorch)torchvision
(for pre-trained models and transformations)PIL
(Python Imaging Library to handle image files)matplotlib
(for displaying images)requests
(for downloading class labels)
You can install these libraries using pip.
PyTorch provides a variety of pre-trained models via the torchvision library. In this tutorial, we use the ResNet-50 model, which has been pre-trained on the ImageNet dataset. We’ll load the model and set it to evaluation mode (which disables certain layers like dropout that are used only during training).
Defining Image Preprocessing
To
use the ResNet model, the input image needs to be preprocessed in the
same way the model was trained. For ResNet, this includes resizing,
center-cropping, and normalizing the image. We’ll use
torchvision.transforms to define the following transformations:
- Resize the image to 256x256 pixels.
- Center-crop the image to 224x224 pixels (ResNet's input size).
- Convert the image to a tensor.
- Normalize the image with the same mean and standard deviation used in ImageNet training.
Load ImageNet Class Labels
The model outputs a tensor of raw scores corresponding to ImageNet class labels. We need to download these labels to interpret the output. We'll fetch the class labels from PyTorch's GitHub repository using the requests library and convert them into a Python list.
The output of class_labels:
Load and Preprocess the Image
Next,
we’ll load a sample image, apply the transformations, and prepare it
for the model. The image is loaded using the PIL library.
Make a Prediction
The
image is ready, we can pass it through the ResNet-50 model to get
predictions. The output will be a tensor of raw scores for each class.
We’ll use the following steps:
- Perform a forward pass through the network.
- Get the predicted class index using torch.max().
- Convert the predicted scores to probabilities using softmax.
- Map the predicted index to the corresponding class label.
Finally, we’ll display the input image alongside its predicted class label and probability using matplotlib.
Conclusion
This tutorial provided an explanation of ResNet model and how to use a pre-trained ResNet-50 model in PyTorch to classify an image. Here, we learned how to:
- The architecture of ResNet model
- Loading the ResNet-50 model.
- Preprocessing an image with the correct transformations.
- Making predictions and interpret the results using class labels.
Complete code for this tutorial is listed below.
Full code listing
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