git clone --recurse-submodules https://github.com/kadirnar/YOLOv6.git
cd YOLOv6
pip install -r requirements.txt
detection_model = Yolov6(
model_path="yolov6s.pt",
source="assert/highway.jpg",
img_size=1280,
conf_thres=0.3,
device="cpu",
)
detection_model.inference()
Single GPU
python YOLOv6/tools/train.py --batch 32 --conf configs/yolov6s.py --data data/coco.yaml --device 0
configs/yolov6n.py
Multi GPUs (DDP mode recommended)
python -m torch.distributed.launch --nproc_per_node 8 YOLOv6/tools/train.py --batch 256 --conf configs/yolov6s.py --data data/coco.yaml --device 0,1,2,3,4,5,6,7
configs/yolov6n.py
- conf: select config file to specify network/optimizer/hyperparameters
- data: prepare COCO dataset and specify dataset paths in data.yaml
Reproduce mAP on COCO val2017 dataset
python YOLOv6/tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s.pt --task val
yolov6n.pt
Model | Size | mAPval 0.5:0.95 |
SpeedV100 fp16 b32 (ms) |
SpeedV100 fp32 b32 (ms) |
SpeedT4 trt fp16 b1 (fps) |
SpeedT4 trt fp16 b32 (fps) |
Params (M) |
Flops (G) |
---|---|---|---|---|---|---|---|---|
YOLOv6-n | 416 640 |
30.8 35.0 |
0.3 0.5 |
0.4 0.7 |
1100 788 |
2716 1242 |
4.3 4.3 |
4.7 11.1 |
YOLOv6-tiny | 640 | 41.3 | 0.9 | 1.5 | 425 | 602 | 15.0 | 36.7 |
YOLOv6-s | 640 | 43.1 | 1.0 | 1.7 | 373 | 520 | 17.2 | 44.2 |
- Comparisons of the mAP and speed of different object detectors are tested on COCO val2017 dataset.
- Refer to Test speed tutorial to reproduce the speed results of YOLOv6.
- Params and Flops of YOLOv6 are estimated on deployed model.
- Speed results of other methods are tested in our environment using official codebase and model if not found from the corresponding official release.