This release requires PyTorch >= v1.0.0 to function properly. Please install the latest version from https://github.com/pytorch/pytorch/releases Breaking Changes There are no breaking changes in this release. Bug Fixes None Added Functionality Video Inference. detect.py now automatically processes both images and videos. Image and video results are saved in their respective formats now (video inference saves new videos). Transfer learning now operates automatically regardless of yolo layer size https://github.com/ultralytics/yolov3/issues/152. Performance https://cloud.google.com/deep-learning-vm/ Machine type: n1-standard-8 (8 vCPUs, 30 GB memory) CPU platform: Intel Skylake GPUs: K80 ($0.198/hr), P4 ($0.279/hr), T4 ($0.353/hr), P100 ($0.493/hr), V100 ($0.803/hr) HDD: 100 GB SSD Dataset: COCO train 2014 GPUs batch_size batch time epoch time epoch cost <i></i> (images) (s/batch) 1 K80 16 1.43s 175min $0.58 1 P4 8 0.51s 125min $0.58 1 T4 16 0.78s 94min $0.55 1 P100 16 0.39s 48min $0.39 2 P100 32 0.48s 29min $0.47 4 P100 64 0.65s 20min $0.65 1 V100 16 0.25s 31min $0.41 2 V100 32 0.29s 18min $0.48 4 V100 64 0.41s 13min $0.70 8 V100 128 0.49s 7min $0.80 TODO (help and PR's welcome!) Add iOS App inference to photos and videos in Camera Roll. Add parameter to switch between 'darknet' and 'power' wh methods. https://github.com/ultralytics/yolov3/issues/168 YAPF linting (including possible wrap to PEP8 79 character-line standard) https://github.com/ultralytics/yolov3/issues/88. Hyperparameter search for loss function constants.