What visual task functionalities does the torchvision library in PyTorch provide?

The torchvision library offers the following functionality for visual tasks:

1. Data loading and preprocessing: includes functions such as loading common datasets (such as MNIST, CIFAR-10, etc.), data augmentation, and image transformations.
2. Model architecture: Pre-trained classic visual models (such as ResNet, VGG, AlexNet, etc) are provided for users to easily perform transfer learning or fine-tuning.
3. Image classification: includes functions for training and evaluating image classification models.
4. Object detection: Support is provided for object detection models such as Faster R-CNN and SSD.
5. Semantic segmentation: support for image semantic segmentation models such as FCN, Unet, etc.
6. Instance segmentation: supports instance segmentation models (such as Mask R-CNN).
7. Image generation: support for image generation models like GANs (Generative Adversarial Networks).
8. Image style transfer: supports image style transfer models.
9. Video categorization: includes support for video categorization models.
10. Dataset and data loading: offers functionality to load and process common visual datasets such as COCO and ImageNet.

Overall, the torchvision library offers a wide range of visual task-related functionalities, making it convenient for users to perform image processing and computer vision tasks.