• YOLOv6
• What's New
• Benchmark
• • Quantized model 🚀
• Quick Start
• • FAQ（Continuously updated）
• YOLOv6
• What's New
• Benchmark
• • Quantized model 🚀
• Quick Start
• • FAQ（Continuously updated）

English | 简体中文

YOLOv6

Implementation of paper:

• YOLOv6 v3.0: A Full-Scale Reloading 🔥
• YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

What's New

• [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.

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 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.

Quantized model 🚀

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.

Quick Start

Install

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.

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 0

Multi 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.mp4

If 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 0

webcam-addr can be local camera number id or rtsp address.

Deployment

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

Tutorials

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

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 TensorRT Windows C++: yolort from Wei Zeng

• 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

FAQ（Continuously updated）

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

English | 简体中文

YOLOv6

Implementation of paper:

• YOLOv6 v3.0: A Full-Scale Reloading 🔥
• YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

What's New

• [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.

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 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.

Quantized model 🚀

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.

Quick Start

Install

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.

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 0

Multi 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.mp4

If 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 0

webcam-addr can be local camera number id or rtsp address.

Deployment

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

Tutorials

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

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 TensorRT Windows C++: yolort from Wei Zeng

• 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

FAQ（Continuously updated）

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