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

Added Functionality

• Various

• 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^val | AP^test | AP₅₀ | Speed_GPU | FPS_GPU || 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^test 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_GPU 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).