v6
版本发布时间: 2019-04-24 23:52:17
ultralytics/yolov3最新发布版本:v9.6.0(2021-11-15 05:26:56)
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
- NMS now screens out nan and inf values which caused it to hang during some edge cases.
Added Functionality
- Rectangular Inference. detect.py now automatically processes images, videos and webcam feeds using rectangular inference, letterboxing to the minimum viable 32-multiple. This speeds up inference by up to 40% on HD video: https://github.com/ultralytics/yolov3/issues/232
- Conv2d + Batchnorm2d Layer Fusion: detect.py now automatically fuses the Conv2d and Batchnorm2d layers in the model before running inference. This speeds up inference by about 5-10%. https://github.com/ultralytics/yolov3/issues/224
- Hyperparameters all parameterized and grouped togethor in train.py now. Genetic Hyperparameter Evolution code added to train.py.
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 |
|---|---|---|---|---|
| (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, as well as 'Flexible', or at least rectangular inference. https://github.com/ultralytics/yolov3/issues/224
- 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.
- Resolve mAP bug: https://github.com/ultralytics/yolov3/issues/222
- Rectangular training. https://github.com/ultralytics/yolov3/issues/232
- Genetic Hyperparameter Evolution. HELP NEEDED HERE. If you have available hardware please contact us, as we need help expanding our hyperparameter search, for the benefit of everyone!