Chapter 22: Computer Vision Project in PyTorch
Abstract : Developing a computer vision project in PyTorch involves a structured approach, leveraging PyTorch's capabilities and its torchvision library for tasks like image classification, object detection, or segmentation. 1. Project Conception and Data Acquisition: Define the Task: Clearly identify the computer vision problem you aim to solve (e.g., classifying dog breeds, detecting cars in images, segmenting medical images). Data Collection/Selection: Obtain a relevant dataset. This could be a pre-existing dataset (like CIFAR-10, ImageNet, COCO) or a custom dataset collected for your specific project. 2. Data Preprocessing and Loading: Transformations: Apply necessary image transformations using torchvision.transforms for data augmentation (e.g., resizing, cropping, normalization, random rotations/flips) to improve model generalization. Dataset Creation: Create a custom dataset class inheriting from torch.uti...