
New features include:
Reference training / evaluation scripts: torchvision now provides, under the references/ folder, scripts for training and evaluation of the following tasks: classification, semantic segmentation, object detection, instance segmentation and person keypoint detection. These serve as a log of how to train a specific model and provide baseline training and evaluation scripts to quickly bootstrap research.
torchvision ops: torchvision now contains custom C++ / CUDA operators. Those operators are specific to computer vision, and make it easier to build object detection models. These operators currently do not support PyTorch script mode, but support for it is planned for in the next release. Some of the ops supported include:

roi_pool (and the module version RoIPool)
roi_align (and the module version RoIAlign)
nms, for non-maximum suppression of bounding boxes
box_iou, for computing the intersection over union metric between two sets of bounding boxes
