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Implementation of Instance Segmentation based on YOLOv6 v4.0 code.
- [2023.09.15] Release YOLOv6-Segmentation. 🚀 Performance
- [2023.04.28] Release YOLOv6Lite models on mobile or CPU. ⭐️ Mobile Benchmark
- [2023.03.10] Release YOLOv6-Face. 🔥 Performance
- [2023.03.02] Update base models to version 3.0.
- [2023.01.06] Release P6 models and enhance the performance of P5 models. ⭐️ Benchmark
- [2022.11.04] Release base models to simplify the training and deployment process.
- [2022.09.06] Customized quantization methods. 🚀 Quantization Tutorial
- [2022.09.05] Release M/L models and update N/T/S models with enhanced performance.
- [2022.06.23] Release N/T/S models with excellent performance.
| Model | Size | mAPval 0.5:0.95 |
SpeedT4 trt fp16 b1 (fps) |
SpeedT4 trt fp16 b32 (fps) |
Params (M) |
FLOPs (G) |
|---|---|---|---|---|---|---|
| YOLOv6-N | 640 | 37.5 | 779 | 1187 | 4.7 | 11.4 |
| YOLOv6-S | 640 | 45.0 | 339 | 484 | 18.5 | 45.3 |
| YOLOv6-M | 640 | 50.0 | 175 | 226 | 34.9 | 85.8 |
| YOLOv6-L | 640 | 52.8 | 98 | 116 | 59.6 | 150.7 |
| YOLOv6-N6 | 1280 | 44.9 | 228 | 281 | 10.4 | 49.8 |
| YOLOv6-S6 | 1280 | 50.3 | 98 | 108 | 41.4 | 198.0 |
| YOLOv6-M6 | 1280 | 55.2 | 47 | 55 | 79.6 | 379.5 |
| YOLOv6-L6 | 1280 | 57.2 | 26 | 29 | 140.4 | 673.4 |
Table Notes
- All checkpoints are trained with self-distillation except for YOLOv6-N6/S6 models trained to 300 epochs without distillation.
- Results of the mAP and speed are evaluated on COCO val2017 dataset with the input resolution of 640×640 for P5 models and 1280x1280 for P6 models.
- Speed is tested with TensorRT 7.2 on T4.
- Refer to Test speed tutorial to reproduce the speed results of YOLOv6.
- Params and FLOPs of YOLOv6 are estimated on deployed models.
| 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 |
- 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.
Install
git clone https://github.com/meituan/YOLOv6
cd YOLOv6
git checkout yolov6-seg
pip install -r requirements.txtTraining
Single GPU
python tools/train.py --batch 8 --conf configs/yolov6s_seg_finetune.py --data data/coco.yaml --device 0Multi 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 valInference
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.mp4If you want to inference on local camera or web camera, you can run:
python tools/infer.py --weights yolov6s_seg.pt --webcam --webcam-addr 0webcam-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.
Tutorials
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 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
If you have any questions, welcome to join our WeChat group to discuss and exchange.
