YOLOv6

Introduction

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

Coming soon

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

Quick Start

Install

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

Inference

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

Training

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, YOLO format coco labes and specify dataset paths in data.yaml
make sure your dataset structure as fellows:

├── coco
│   ├── annotations
│   │   ├── instances_train2017.json
│   │   └── instances_val2017.json
│   ├── images
│   │   ├── train2017
│   │   └── val2017
│   ├── labels
│   │   ├── train2017
│   │   ├── val2017
│   ├── LICENSE
│   ├── README.txt

Evaluation

Reproduce mAP on COCO val2017 dataset

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

Resume

If your training process is corrupted, you can resume training by

# single GPU traning.
python tools/train.py --resume
# multi GPU training.
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --resume

Your can also specify a checkpoint path to --resume parameter by

# remember replace /path/to/your/checkpoint/path to the checkpoint path which you want to resume training.
--resume /path/to/your/checkpoint/path

Deployment

Tutorials

Benchmark

Model	Size	mAP^val 0.5:0.95	Speed^{V100 fp16 b32 (ms)}	Speed^{V100 fp32 b32 (ms)}	Speed^{T4 trt fp16 b1 (fps)}	Speed^{T4 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.

Third-party resources

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
YOLOv6 Quantization and Auto Compression Example YOLOv6-ACT from PaddleSlim
YOLOv6 web demo on Huggingface Spaces with Gradio.

1chimaruGin / YOLOv6