YOLOv5 Now Open-Sourced 🚀 #1352
Description
Ultralytics has open-sourced YOLOv5 at https://github.com/ultralytics/yolov5, featuring faster, lighter and more accurate object detection. YOLOv5 is recommended for all new projects.
** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from google/automl at batch size 8.
- August 13, 2020: v3.0 release: nn.Hardswish() activations, data autodownload, native AMP.
- 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.
Pretrained Checkpoints
| Model | size | APval | APtest | AP50 | SpeedV100 | FPSV100 | params | GFLOPS | |
|---|---|---|---|---|---|---|---|---|---|
| YOLOv5s | 640 | 36.8 | 36.8 | 55.6 | 2.2ms | 455 | 7.3M | 17.0 | |
| YOLOv5m | 640 | 44.5 | 44.5 | 63.1 | 2.9ms | 345 | 21.4M | 51.3 | |
| YOLOv5l | 640 | 48.1 | 48.1 | 66.4 | 3.8ms | 264 | 47.0M | 115.4 | |
| YOLOv5x | 640 | 50.1 | 50.1 | 68.7 | 6.0ms | 167 | 87.7M | 218.8 | |
| YOLOv5x + TTA | 832 | 51.9 | 51.9 | 69.6 | 24.9ms | 40 | 87.7M | 1005.3 |
** APtest 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 640 --conf 0.001
** SpeedGPU 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).
** Test Time Augmentation (TTA) runs at 3 image sizes. Reproduce by python test.py --data coco.yaml --img 832 --augment
For more information and to get started with YOLOv5 please visit https://github.com/ultralytics/yolov5. Thank you!