/YOLOv6

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

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 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