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Description
👋 Hello! Thanks for visiting! 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.
YOLOv5-P5 640 Figure (click to expand)
Figure Notes (click to expand)
- 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.
- Reproduce by
python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt
- April 11, 2021: v5.0 release: YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations.
- January 5, 2021: v4.0 release: nn.SiLU() activations, Weights & Biases logging, PyTorch Hub integration.
- 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.
Pretrained Checkpoints
| Model | size (pixels) |
mAPval 0.5:0.95 |
mAPtest 0.5:0.95 |
mAPval 0.5 |
Speed V100 (ms) |
params (M) |
FLOPS 640 (B) |
|
|---|---|---|---|---|---|---|---|---|
| YOLOv5s | 640 | 36.7 | 36.7 | 55.4 | 2.0 | 7.3 | 17.0 | |
| YOLOv5m | 640 | 44.5 | 44.5 | 63.1 | 2.7 | 21.4 | 51.3 | |
| YOLOv5l | 640 | 48.2 | 48.2 | 66.9 | 3.8 | 47.0 | 115.4 | |
| YOLOv5x | 640 | 50.4 | 50.4 | 68.8 | 6.1 | 87.7 | 218.8 | |
| YOLOv5s6 | 1280 | 43.3 | 43.3 | 61.9 | 4.3 | 12.7 | 17.4 | |
| YOLOv5m6 | 1280 | 50.5 | 50.5 | 68.7 | 8.4 | 35.9 | 52.4 | |
| YOLOv5l6 | 1280 | 53.4 | 53.4 | 71.1 | 12.3 | 77.2 | 117.7 | |
| YOLOv5x6 | 1280 | 54.4 | 54.4 | 72.0 | 22.4 | 141.8 | 222.9 | |
| YOLOv5x6 TTA | 1280 | 55.0 | 55.0 | 72.0 | 70.8 | - | - |
Table Notes (click to expand)
- APtest denotes COCO test-dev2017 server results, all other AP results denote val2017 accuracy.
- AP values are for single-model single-scale unless otherwise noted. Reproduce mAP by
python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65 - SpeedGPU averaged over 5000 COCO val2017 images using a GCP n1-standard-16 V100 instance, and includes FP16 inference, postprocessing and NMS. Reproduce speed by
python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 - All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
- Test Time Augmentation (TTA) includes reflection and scale augmentation. Reproduce TTA by
python test.py --data coco.yaml --img 1536 --iou 0.7 --augment
For more information and to get started with YOLOv5 🚀 please visit https://github.com/ultralytics/yolov5. Thank you!