This release aggregates various minor bug fixes and performance improvements since the main v3.0 release and incorporates PyTorch 1.7.0 compatibility updates. v3.1 models share weights with v3.0 models but contain minor module updates (inplace fields for nn.Hardswish() activations) for native PyTorch 1.7.0 compatibility. Breaking Changes 'GIoU' hyperparameter has been renamed to 'box' to better reflect a criteria-agnostic regression loss term (https://github.com/ultralytics/yolov5/pull/1120) Bug Fixes PyTorch 1.7 compatibility update. torch>=1.6.0 required, torch>=1.7.0 recommended (https://github.com/ultralytics/yolov5/pull/1233) GhostConv module bug fix (https://github.com/ultralytics/yolov5/pull/1176) Rectangular padding min stride bug fix from 64 to 32 (https://github.com/ultralytics/yolov5/pull/1165) Mosaic4 bug fix (https://github.com/ultralytics/yolov5/pull/1021) Logging directory runs/exp bug fix (https://github.com/ultralytics/yolov5/pull/978) Various additional Added Functionality PyTorch Hub functionality with YOLOv5 .autoshape() method added (https://github.com/ultralytics/yolov5/pull/1210) Autolabelling addition and standardization across detect.py and test.py (https://github.com/ultralytics/yolov5/pull/1182) Precision-Recall Curve automatic plotting when testing (https://github.com/ultralytics/yolov5/pull/1107) Self-host VOC dataset for more reliable access and faster downloading (https://github.com/ultralytics/yolov5/pull/1077) Adding option to output autolabel confidence with --save-conf in test.py and detect.py (https://github.com/ultralytics/yolov5/pull/994) Google App Engine deployment option (https://github.com/ultralytics/yolov5/pull/964) Infinite Dataloader for faster training (https://github.com/ultralytics/yolov5/pull/876) Various additional