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YOLOv6-Segmentation

Implementation of Instance Segmentation based on YOLOv6 v4.0 code.

Performance on MSCOCO

Model	Size	mAPbox 50-95	mAPmask 50-95	SpeedT4 trt fp16 b1 (fps)	Params (M)	FLOPs (G)
YOLOv6-N	640	35.3	31.2	645	4.9	7.0
YOLOv6-S	640	44.0	38.0	292	19.6	27.7
YOLOv6-M	640	48.2	41.3	148	37.1	54.3
YOLOv6-L	640	51.1	43.7	93	63.6	95.5
YOLOv6-X	640	52.2	44.8	47	119.1	175.5

Table Notes

• All checkpoints are trained from scratch on COCO for 300 epochs without distillation.
• Results of the mAP and speed are evaluated on COCO val2017 dataset with the input resolution of 640×640.
• Speed is tested with TensorRT 8.5 on T4 without post-processing.
• Refer to Test speed tutorial to reproduce the speed results of YOLOv6.
• Params and FLOPs of YOLOv6 are estimated on deployed models.

Quick Start

Install

git clone https://github.com/meituan/YOLOv6
cd YOLOv6
git checkout yolov6-seg
pip install -r requirements.txt

Training

Single GPU

python tools/train.py --batch 8 --conf configs/yolov6s_seg_finetune.py --data data/coco.yaml --device 0

Multi GPUs (DDP mode recommended)

python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 64 --conf configs/yolov6s_seg_finetune.py --data data/coco.yaml --device 0,1,2,3,4,5,6,7

• fuse_ab: Not supported in current version
• conf: select config file to specify network/optimizer/hyperparameters. We recommend to apply yolov6n/s/m/l_finetune.py when training on your custom dataset.
• data: prepare dataset and specify dataset paths in data.yaml ( COCO, YOLO format coco labels )
• make sure your dataset structure as follows:

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

Evaluation

Reproduce mAP on COCO val2017 dataset with 640×640 resolution

python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s_seg.pt --task val

Inference

First, download a pretrained model from the YOLOv6 release or use your trained model to do inference.

Second, run inference with tools/infer.py

python tools/infer.py --weights yolov6s_seg.pt --source img.jpg / imgdir / video.mp4

If you want to inference on local camera or web camera, you can run:

python tools/infer.py --weights yolov6s_seg.pt --webcam --webcam-addr 0

webcam-addr can be local camera number id or rtsp address. Maybe you want to eval a solo-head model, remember to add the --issolo parameter.

Deployment

• ONNX
• OpenCV Python/C++
• OpenVINO
• TensorRT
• NCNN
• Android

Tutorials

• User Guide(zh_CN)
• Train COCO Dataset
• Train custom data
• Test speed
• Tutorial of Quantization for YOLOv6

Third-party resources

• YOLOv6 Training with Amazon Sagemaker: yolov6-sagemaker from ashwincc

• 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 web demo on Huggingface Spaces with Gradio.

• Interactive demo on DagsHub with Streamlit

• Tutorial: How to train YOLOv6 on a custom dataset

• YouTube Tutorial: How to train YOLOv6 on a custom dataset

• Demo of YOLOv6 inference on Google Colab

• Blog post: YOLOv6 Object Detection – Paper Explanation and Inference

FAQ（Continuously updated）

If you have any questions, welcome to join our WeChat group to discuss and exchange.