# Python examples

- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/vision/blob/master/examples/python/tensor_transforms.ipynb)
[Examples of Tensor Images transformations](https://github.com/pytorch/vision/blob/master/examples/python/tensor_transforms.ipynb)
- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/vision/blob/master/examples/python/video_api.ipynb)
[Example of VideoAPI](https://github.com/pytorch/vision/blob/master/examples/python/video_api.ipynb)


Prior to v0.8.0, transforms in torchvision have traditionally been PIL-centric and presented multiple limitations due to 
that. Now, since v0.8.0, transforms implementations are Tensor and PIL compatible and we can achieve the following new 
features:
- transform multi-band torch tensor images (with more than 3-4 channels) 
- torchscript transforms together with your model for deployment
- support for GPU acceleration
- batched transformation such as for videos
- read and decode data directly as torch tensor with torchscript support (for PNG and JPEG image formats)

Furthermore, previously we used to provide a very high-level API for video decoding which left little control to the user. We're now expanding that API (and replacing it in the future) with a lower-level API that allows the user a frame-based access to a video.