Chapter 20: Image Classification Project with PyTorch
Abstract: An image classification project with PyTorch typically involves several key stages: 1. Data Preparation: Dataset Loading: Load your image dataset. This can involve using torchvision.datasets for common datasets (e.g., CIFAR-10, Fashion MNIST) or creating a custom Dataset class for your specific data. Data Augmentation and Preprocessing: Apply transformations to your images using torchvision.transforms . This includes resizing, cropping, normalization (e.g., ToTensor , Normalize ), and data augmentation techniques like random rotations or flips to improve model generalization. DataLoader Creation: Create DataLoader objects to efficiently load and batch your data during training and evaluation. 2. Model Definition: Choose/Define a CNN Architecture: Select a suitable Convolutional Neural Network (CNN) architecture. This could be a pre-trained model from torchvision.mo...