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YOLOv6
Implementation of paper:
- YOLOv6 v3.0: A Full-Scale Reloading ๐ฅ
- YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
What's New
- [2023.09.15] Release YOLOv6-Segmentation. ๐ Performance
- [2023.04.28] Release YOLOv6Lite models on mobile or CPU. โญ๏ธ Mobile Benchmark
- [2023.03.10] Release YOLOv6-Face. ๐ฅ Performance
- [2023.03.02] Update base models to version 3.0.
- [2023.01.06] Release P6 models and enhance the performance of P5 models. โญ๏ธ Benchmark
- [2022.11.04] Release base models to simplify the training and deployment process.
- [2022.09.06] Customized quantization methods. ๐ 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.
Benchmark
| 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](https://cocodataset.org/#download) dataset with the input resolution of 640ร640 for P5 models and 1280x1280 for P6 models. - Speed is tested with TensorRT 7.2 on T4. - Refer to [Test speed](./docs/Test_speed.md) tutorial to reproduce the speed results of YOLOv6. - Params and FLOPs of YOLOv6 are estimated on deployed models.Legacy models
| Model | Size | mAPval0.5:0.95 | SpeedT4
trt fp16 b1
(fps) | SpeedT4
trt fp16 b32
(fps) | Params
(M) | FLOPs
(G) | | :----------------------------------------------------------- | ---- | :------------------------------------ | --------------------------------------- | ---------------------------------------- | -------------------- | ------------------- | | [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6n.pt) | 640 | 35.9300e
36.3400e | 802 | 1234 | 4.3 | 11.1 | | [**YOLOv6-T**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6t.pt) | 640 | 40.3300e
41.1400e | 449 | 659 | 15.0 | 36.7 | | [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6s.pt) | 640 | 43.5300e
43.8400e | 358 | 495 | 17.2 | 44.2 | | [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6m.pt) | 640 | 49.5 | 179 | 233 | 34.3 | 82.2 | | [**YOLOv6-L-ReLU**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6l_relu.pt) | 640 | 51.7 | 113 | 149 | 58.5 | 144.0 | | [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6l.pt) | 640 | 52.5 | 98 | 121 | 58.5 | 144.0 | - Speed is tested with TensorRT 7.2 on T4. ### Quantized model ๐ | Model | Size | Precision | mAPval
0.5:0.95 | SpeedT4
trt b1
(fps) | SpeedT4
trt b32
(fps) | | :-------------------- | ---- | --------- | :----------------------- | ---------------------------------- | ----------------------------------- | | **YOLOv6-N RepOpt** | 640 | INT8 | 34.8 | 1114 | 1828 | | **YOLOv6-N** | 640 | FP16 | 35.9 | 802 | 1234 | | **YOLOv6-T RepOpt** | 640 | INT8 | 39.8 | 741 | 1167 | | **YOLOv6-T** | 640 | FP16 | 40.3 | 449 | 659 | | **YOLOv6-S RepOpt** | 640 | INT8 | 43.3 | 619 | 924 | | **YOLOv6-S** | 640 | FP16 | 43.5 | 377 | 541 | - Speed is tested with TensorRT 8.4 on T4. - Precision is figured on models for 300 epochs.
Mobile Benchmark
| Model | Size | mAPval 0.5:0.95 |
sm8350 (ms) |
mt6853 (ms) |
sdm660 (ms) |
Params (M) |
FLOPs (G) |
|---|---|---|---|---|---|---|---|
| YOLOv6Lite-S | 320*320 | 22.4 | 7.99 | 11.99 | 41.86 | 0.55 | 0.56 |
| YOLOv6Lite-M | 320*320 | 25.1 | 9.08 | 13.27 | 47.95 | 0.79 | 0.67 |
| YOLOv6Lite-L | 320*320 | 28.0 | 11.37 | 16.20 | 61.40 | 1.09 | 0.87 |
| YOLOv6Lite-L | 320*192 | 25.0 | 7.02 | 9.66 | 36.13 | 1.09 | 0.52 |
| YOLOv6Lite-L | 224*128 | 18.9 | 3.63 | 4.99 | 17.76 | 1.09 | 0.24 |
Table Notes
- From the perspective of model size and input image ratio, we have built a series of models on the mobile terminal to facilitate flexible applications in different scenarios. - All checkpoints are trained with 400 epochs without distillation. - Results of the mAP and speed are evaluated on [COCO val2017](https://cocodataset.org/#download) dataset, and the input resolution is the Size in the table. - Speed is tested on MNN 2.3.0 AArch64 with 2 threads by arm82 acceleration. The inference warm-up is performed 10 times, and the cycle is performed 100 times. - Qualcomm 888(sm8350), Dimensity 720(mt6853) and Qualcomm 660(sdm660) correspond to chips with different performances at the high, middle and low end respectively, which can be used as a reference for model capabilities under different chips. - Refer to [Test NCNN Speed](./docs/Test_NCNN_speed.md) tutorial to reproduce the NCNN speed results of YOLOv6Lite.Quick Start
Install
```shell git clone https://github.com/meituan/YOLOv6 cd YOLOv6 pip install -r requirements.txt ```Reproduce our results on COCO
Please refer to [Train COCO Dataset](./docs/Train_coco_data.md).Finetune on custom data
Single GPU ```shell # P5 models python tools/train.py --batch 32 --conf configs/yolov6s_finetune.py --data data/dataset.yaml --fuse_ab --device 0 # P6 models python tools/train.py --batch 32 --conf configs/yolov6s6_finetune.py --data data/dataset.yaml --img 1280 --device 0 ``` Multi GPUs (DDP mode recommended) ```shell # P5 models python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 256 --conf configs/yolov6s_finetune.py --data data/dataset.yaml --fuse_ab --device 0,1,2,3,4,5,6,7 # P6 models python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 128 --conf configs/yolov6s6_finetune.py --data data/dataset.yaml --img 1280 --device 0,1,2,3,4,5,6,7 ``` - fuse_ab: add anchor-based auxiliary branch and use Anchor Aided Training Mode (Not supported on P6 models currently) - 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](http://cocodataset.org), [YOLO format coco labels](https://github.com/meituan/YOLOv6/releases/download/0.1.0/coco2017labels.zip) ) - make sure your dataset structure as follows: ``` โโโ coco โ โโโ annotations โ โ โโโ instances_train2017.json โ โ โโโ instances_val2017.json โ โโโ images โ โ โโโ train2017 โ โ โโโ val2017 โ โโโ labels โ โ โโโ train2017 โ โ โโโ val2017 โ โโโ LICENSE โ โโโ README.txt ``` YOLOv6 supports different input resolution modes. For details, see [How to Set the Input Size](./docs/About_training_size.md).Resume training
If your training process is corrupted, you can resume training by ``` # single GPU training. python tools/train.py --resume # multi GPU training. python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --resume ``` Above command will automatically find the latest checkpoint in YOLOv6 directory, then resume the training process. Your can also specify a checkpoint path to `--resume` parameter by ``` # remember to replace /path/to/your/checkpoint/path to the checkpoint path which you want to resume training. --resume /path/to/your/checkpoint/path ``` This will resume from the specific checkpoint you provide.Evaluation
Reproduce mAP on COCO val2017 dataset with 640ร640 or 1280x1280 resolution ```shell # P5 models python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s.pt --task val --reproduce_640_eval # P6 models python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s6.pt --task val --reproduce_640_eval --img 1280 ``` - verbose: set True to print mAP of each classes. - do_coco_metric: set True / False to enable / disable pycocotools evaluation method. - do_pr_metric: set True / False to print or not to print the precision and recall metrics. - config-file: specify a config file to define all the eval params, for example: [yolov6n_with_eval_params.py](configs/experiment/yolov6n_with_eval_params.py)Inference
First, download a pretrained model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases/tag/0.4.0) or use your trained model to do inference. Second, run inference with `tools/infer.py` ```shell # P5 models python tools/infer.py --weights yolov6s.pt --source img.jpg / imgdir / video.mp4 # P6 models python tools/infer.py --weights yolov6s6.pt --img 1280 1280 --source img.jpg / imgdir / video.mp4 ``` If you want to inference on local camera or web camera, you can run: ```shell # P5 models python tools/infer.py --weights yolov6s.pt --webcam --webcam-addr 0 # P6 models python tools/infer.py --weights yolov6s6.pt --img 1280 1280 --webcam --webcam-addr 0 ``` `webcam-addr` can be local camera number id or rtsp address.Deployment
* [ONNX](./deploy/ONNX) * [OpenCV Python/C++](./deploy/ONNX/OpenCV) * [OpenVINO](./deploy/OpenVINO) * [TensorRT](./deploy/TensorRT) * [NCNN](./deploy/NCNN) * [Android](./deploy/NCNN/Android)Tutorials
* [User Guide(zh_CN)](https://yolov6-docs.readthedocs.io/zh_CN/latest/) * [Train COCO Dataset](./docs/Train_coco_data.md) * [Train custom data](./docs/Train_custom_data.md) * [Test speed](./docs/Test_speed.md) * [Tutorial of Quantization for YOLOv6](./docs/Tutorial%20of%20Quantization.md)Third-party resources
* YOLOv6 Training with Amazon Sagemaker: [yolov6-sagemaker](https://github.com/ashwincc/yolov6-sagemaker) from [ashwincc](https://github.com/ashwincc) * YOLOv6 NCNN Android app demo: [ncnn-android-yolov6](https://github.com/FeiGeChuanShu/ncnn-android-yolov6) from [FeiGeChuanShu](https://github.com/FeiGeChuanShu) * YOLOv6 ONNXRuntime/MNN/TNN C++: [YOLOv6-ORT](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ort/cv/yolov6.cpp), [YOLOv6-MNN](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/mnn/cv/mnn_yolov6.cpp) and [YOLOv6-TNN](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_yolov6.cpp) from [DefTruth](https://github.com/DefTruth) * YOLOv6 TensorRT Python: [yolov6-tensorrt-python](https://github.com/Linaom1214/TensorRT-For-YOLO-Series) from [Linaom1214](https://github.com/Linaom1214) * YOLOv6 TensorRT Windows C++: [yolort](https://github.com/zhiqwang/yolov5-rt-stack/tree/main/deployment/tensorrt-yolov6) from [Wei Zeng](https://github.com/Wulingtian) * [YOLOv6 web demo](https://huggingface.co/spaces/nateraw/yolov6) on [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). [](https://huggingface.co/spaces/nateraw/yolov6) * [Interactive demo](https://yolov6.dagshubusercontent.com/) on [DagsHub](https://dagshub.com) with [Streamlit](https://github.com/streamlit/streamlit) * Tutorial: [How to train YOLOv6 on a custom dataset](https://blog.roboflow.com/how-to-train-yolov6-on-a-custom-dataset/)
* YouTube Tutorial: [How to train YOLOv6 on a custom dataset](https://youtu.be/fFCWrMFH2UY)
* Demo of YOLOv6 inference on Google Colab [](https://colab.research.google.com/github/mahdilamb/YOLOv6/blob/main/inference.ipynb)
* Blog post: [YOLOv6 Object Detection โ Paper Explanation and Inference](https://learnopencv.com/yolov6-object-detection/)
FAQ๏ผContinuously updated๏ผ
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