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

• ONNX
• OpenVINO
• Partial Quantization

Tutorials

• Train custom data
• Test speed
• Tutorial of RepOpt for YOLOv6

Benchmark

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.

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.
• Tutorial: How to train YOLOv6 on a custom dataset
• Demo of YOLOv6 inference on Google Colab