v7
版本发布时间: 2020-05-05 03:24:57
ultralytics/yolov3最新发布版本:v9.6.0(2021-11-15 05:26:56)
This release requires PyTorch >= v1.4 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
- Various
Added Functionality
- Improved training and test ground truth and prediction plotting. https://github.com/ultralytics/yolov3/pull/1114
- Increased augmentation speed. https://github.com/ultralytics/yolov3/pull/1110
- Improved Tensorboard integration.
- Auto class hyperparameter update based on dataset class count.
- Inference time augmentation option added now with
--augmentargument in test.py and detect.py. - Rectangular training with
--rectargument in train.py
Speed
https://cloud.google.com/deep-learning-vm/
Machine type: preemptible n1-standard-8 (8 vCPUs, 30 GB memory)
CPU platform: Intel Skylake
GPUs: K80 ($0.14/hr), T4 ($0.11/hr), V100 ($0.74/hr) CUDA with Nvidia Apex FP16/32
HDD: 300 GB SSD
Dataset: COCO train 2014 (117,263 images)
Model: yolov3-spp.cfg
Command: python3 train.py --data coco2017.data --img 416 --batch 32
| GPU | n | --batch-size |
img/s | epoch time |
epoch cost |
|---|---|---|---|---|---|
| K80 | 1 | 32 x 2 | 11 | 175 min | $0.41 |
| T4 | 1 2 |
32 x 2 64 x 1 |
41 61 |
48 min 32 min |
$0.09 $0.11 |
| V100 | 1 2 |
32 x 2 64 x 1 |
122 178 |
16 min 11 min |
$0.21 $0.28 |
| 2080Ti | 1 2 |
32 x 2 64 x 1 |
81 140 |
24 min 14 min |
- - |
mAP
| Size | COCO mAP @0.5...0.95 |
COCO mAP @0.5 |
|
|---|---|---|---|
| YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics |
320 | 14.0 28.7 30.5 37.7 |
29.1 51.8 52.3 56.8 |
| YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics |
416 | 16.0 31.2 33.9 41.2 |
33.0 55.4 56.9 60.6 |
| YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics |
512 | 16.6 32.7 35.6 42.6 |
34.9 57.7 59.5 62.4 |
| YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics |
608 | 16.6 33.1 37.0 43.1 |
35.4 58.2 60.7 62.8 |
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