torchvision

Torchvision is a library in PyTorch that provides useful tools and functionalities for computer vision tasks. It includes pre-trained models, datasets, transforms, and utilities for image and video processing. Torchvision makes it easier for researchers and developers to work with computer vision applications, such as object detection, image classification, and segmentation. Some of the popular features of Torchvision include:

1. Pre-trained models: Torchvision includes pre-trained models for various computer vision tasks such as image classification, object detection, and segmentation. These models can be fine-tuned for specific use cases or used as feature extractors for transfer learning.

2. Datasets: Torchvision provides access to popular datasets such as ImageNet, CIFAR, and COCO. These datasets can be easily loaded and preprocessed using the built-in transforms.

3. Transforms: Torchvision includes a wide range of image and video transformations such as resizing, cropping, flipping, and normalization. These transforms can be applied to the input data before feeding it into the model.

4. Utilities: Torchvision provides various utility functions for visualization, evaluation, and data loading. These functions can be used to analyze the performance of the model and debug any issues.

Overall, Torchvision is a powerful library that simplifies the development of computer vision applications in PyTorch.