YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.

YOLOv6-nano achieves 35.0 mAP on COCO val2017 dataset with 1242 FPS on T4 using TensorRT FP16 for bs32 inference, and YOLOv6-s achieves 43.1 mAP on COCO val2017 dataset with 520 FPS on T4 using TensorRT FP16 for bs32 inference.

YOLOv6 is composed of the following methods:

• Hardware-friendly Design for Backbone and Neck
• Efficient Decoupled Head with SIoU Loss

• YOLOv6 m/l/x model.
• Deployment for MNN/TNN/NCNN/CoreML...
• Quantization tools

git clone https://github.com/meituan/YOLOv6
cd YOLOv6
pip install -r requirements.txt

First, download a pretrained model from the YOLOv6 release

Second, run inference with tools/infer.py

python tools/infer.py --weights yolov6s.pt --source img.jpg / imgdir
                                yolov6n.pt

Single GPU

python 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 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 tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s.pt --task val
                                                                yolov6n.pt

• ONNX
• OpenVINO

• Train custom data
• Test speed

• 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.

• YOLOv6 NCNN Android app demo: ncnn-android-yolov6 from FeiGeChuanShu
• YOLOv6 ONNXRuntime/MNN/TNN C++: YOLOv6-ORT, YOLOv6-MNN and YOLOv6-TNN from DefTruth
• YOLOv6 TensorRT Python: yolov6-tensorrt-python from Linaom1214
• YOLOv6 TensorRT Windows C++: yolort from Wei Zeng