ultralytics/yolov5: v2.0
Creators
- 1. @ultralytics
- 2. Jacobs JETS @ NASA Johnson Space Center
- 3. CTU in Prague
- 4. alibaba
- 5. innAIte technologies
- 6. University of Milan-Bicocca
- 7. Carnegie Mellon University
- 8. Topia Systems Ltd
- 9. Digitalist @digitalist-se
- 10. Shanghai Jiao Tong University
Description
Breaking Changes
IMPORTANT: v2.0 release contains breaking changes. Models trained with earlier versions will not operate correctly with v2.0. The last commit before v2.0 that operates correctly with all earlier pretrained models is: https://github.com/ultralytics/yolov5/tree/5e970d45c44fff11d1eb29bfc21bed9553abf986
To clone last commit prior to v2.0:
git clone https://github.com/ultralytics/yolov5 # clone repo
cd yolov5
git reset --hard 5e970d4 # last commit before v2.0
Bug Fixes
- Various
- Various
<img src="https://user-images.githubusercontent.com/26833433/85340570-30360a80-b49b-11ea-87cf-bdf33d53ae15.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
- July 23, 2020: v2.0 release: improved model definition, training and mAP.
- June 22, 2020: PANet updates: new heads, reduced parameters, improved speed and mAP 364fcfd.
- June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
- June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
- May 27, 2020: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
- April 1, 2020: Start development of future compound-scaled YOLOv3/YOLOv4-based PyTorch models.
AP<sup>test</sup> denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy. All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 672 --conf 0.001
Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1
All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
Files
ultralytics/yolov5-v2.0.zip
Files
(3.2 MB)
Name | Size | Download all |
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md5:5d44fb1b6d0c33bf9b9da7617c3c3c1c
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3.2 MB | Preview Download |
Additional details
Related works
- Is supplement to
- https://github.com/ultralytics/yolov5/tree/v2.0 (URL)