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Open In Colab Open In Kaggle

YOLOv6

Implementation of paper:

What's New

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.

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 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 tutorial to reproduce the NCNN speed results of YOLOv6Lite.

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

YOLOv6 supports different input resolution modes. For details, see How to Set the Input Size.

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
Tutorials
Third-party resources

FAQ（Continuously updated）

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