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Implementation of paper:
- YOLOv6 v3.0: A Full-Scale Reloading 🔥
- YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
- [2023.01.06] Release P6 models and enhance the performance of P5 models. ⭐️ Benchmark
- Renew the neck of the detector with a BiC module and SimCSPSPPF Block.
- Propose an anchor-aided training (AAT) strategy.
- Involve a new self-distillation strategy for small models of YOLOv6.
- Expand YOLOv6 and hit a new SOTA performance on the COCO dataset.
- [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.
Legacy models
| Model | Size | mAPval 0.5:0.95 |
SpeedT4 trt fp16 b1 (fps) |
SpeedT4 trt fp16 b32 (fps) |
Params (M) |
FLOPs (G) |
|---|---|---|---|---|---|---|
| YOLOv6-N | 640 | 35.9300e 36.3400e |
802 | 1234 | 4.3 | 11.1 |
| YOLOv6-T | 640 | 40.3300e 41.1400e |
449 | 659 | 15.0 | 36.7 |
| YOLOv6-S | 640 | 43.5300e 43.8400e |
358 | 495 | 17.2 | 44.2 |
| YOLOv6-M | 640 | 49.5 | 179 | 233 | 34.3 | 82.2 |
| YOLOv6-L-ReLU | 640 | 51.7 | 113 | 149 | 58.5 | 144.0 |
| YOLOv6-L | 640 | 52.5 | 98 | 121 | 58.5 | 144.0 |
- Speed is tested with TensorRT 7.2 on T4.
| 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.
Install
git clone https://github.com/meituan/YOLOv6
cd YOLOv6
pip install -r requirements.txtReproduce our results on COCO
Please refer to Train COCO Dataset.
Finetune on custom data
Single GPU
# 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 0Multi GPUs (DDP mode recommended)
# 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, 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
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 640×640 or 1280x1280 resolution
# 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
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
# 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.mp4If you want to inference on local camera or web camera, you can run:
# 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 0webcam-addr can be local camera number id or rtsp address.
Deployment
Tutorials
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 web demo on Huggingface Spaces with Gradio.
-
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.
