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Software Open Access

ultralytics/yolov5: v2.0

Glenn Jocher; Alex Stoken; Jirka Borovec; ChristopherSTAN; Liu Changyu; Adam Hogan; Laughing; NanoCode012; yxNONG; Laurentiu Diaconu; lorenzomammana; wanghaoyang0106; ml5ah; Doug; Jake Poznanski; Lijun Yu 于力军; changyu98; Prashant Rai; Russ Ferriday; Trevor Sullivan; Eduard Reñé Claramunt; pritul dave; yzchen

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:

To clone last commit prior to v2.0:

git clone  # clone repo
cd yolov5
git reset --hard 5e970d4  # last commit before v2.0
Bug Fixes
  • Various
Added Functionality
  • Various

<img src="" 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.
Pretrained Checkpoints Model AP<sup>val</sup> AP<sup>test</sup> AP<sub>50</sub> Speed<sub>GPU</sub> FPS<sub>GPU</sub> params FLOPS YOLOv5s 36.1 36.1 55.3 2.1ms 476 7.5M 13.2B YOLOv5m 43.5 43.5 62.5 3.0ms 333 21.8M 39.4B YOLOv5l 47.0 47.1 65.6 3.9ms 256 47.8M 88.1B YOLOv5x 49.0 49.0 67.4 6.1ms 164 89.0M 166.4B YOLOv3-SPP 45.6 45.5 65.2 4.5ms 222 63.0M 118.0B

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 --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 --data coco.yaml --img 640 --conf 0.1 All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).

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