PyTorch is a flexible framework for computer vision tasks, offering tools for loading data, building models, and training systems efficiently. The core components include the torchvision library, which provides datasets, model architectures, and image transformations. A typical workflow involves preparing data with transformations, defining a neural network (either a pre-trained model or a custom design), and training it using PyTorch’s automatic differentiation and optimizer classes. For example, you might load the CIFAR-10 dataset, apply resizing and normalization, train a ResNet model, and evaluate its accuracy.
Data handling is streamlined using Dataset and DataLoader classes. torchvision.datasets includes common datasets like MNIST or ImageNet, which can be preprocessed using transforms.Compose to chain operations like converting images to tensors (ToTensor()) or normalizing pixel values (Normalize(mean, std)). The DataLoader batches data and supports shuffling, parallel loading, and memory efficiency. For instance, loading CIFAR-10 might involve resizing images to 32x32, converting them to tensors, and normalizing RGB channels. The DataLoader then creates batches of 64 images, enabling efficient iteration during training.
For model building, PyTorch offers pre-trained architectures (e.g., ResNet, VGG) via torchvision.models, which can be fine-tuned by replacing their final layers. Alternatively, you can define custom models using nn.Module, adding layers like Conv2d, MaxPool2d, and Linear. Training involves defining a loss function (e.g., CrossEntropyLoss), an optimizer (e.g., SGD or Adam), and iterating over the data. A basic training loop includes forward passes to compute predictions, calculating loss, backpropagating gradients with loss.backward(), and updating weights with optimizer.step(). After training, models are evaluated on validation data to measure accuracy or other metrics, completing the end-to-end process.