matrixgame2018 / YOLOv6

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

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Implementation of paper - YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

Introduction

YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.

YOLOv6 has a series of models for various industrial scenarios, including N/T/S/M/L, which the architectures vary considering the model size for better accuracy-speed trade-off. And some Bag-of-freebies methods are introduced to further improve the performance, such as self-distillation and more training epochs. For industrial deployment, we adopt QAT with channel-wise distillation and graph optimization to pursue extreme performance.

YOLOv6-N hits 35.9% AP on COCO dataset with 1234 FPS on T4. YOLOv6-S strikes 43.5% AP with 495 FPS, and the quantized YOLOv6-S model achieves 43.3% AP at a accelerated speed of 869 FPS on T4. YOLOv6-T/M/L also have excellent performance, which show higher accuracy than other detectors with the similar inference speed.

What's New

  • Release M/L models and update N/T/S models with enhanced performance.⭐️ Benchmark
  • 2x faster training time.
  • Fix the degration of performance when evaluating on 640x640 inputs.
  • Customized quantization methods. πŸš€ Quantization Tutorial

Quick Start

Install

git clone https://github.com/meituan/YOLOv6
cd YOLOv6
pip install -r requirements.txt

Inference

First, download a pretrained model from the YOLOv6 release

Second, run inference with tools/infer.py

python tools/infer.py --weights yolov6s.pt --source img.jpg / imgdir / video.mp4

Training

Single GPU

python tools/train.py --batch 32 --conf configs/yolov6s.py --data data/coco.yaml --device 0

Multi GPUs (DDP mode recommended)

python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 256 --conf configs/yolov6s.py --data data/coco.yaml --device 0,1,2,3,4,5,6,7
Reproduce our results on COCO

For nano model

python -m torch.distributed.launch --nproc_per_node 4 tools/train.py \
									--batch 128 \
									--conf configs/yolov6n.py \
									--data data/coco.yaml \
									--epoch 400 \
									--device 0,1,2,3 \
									--name yolov6n_coco

For s/tiny model

python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \
									--batch 256 \
									--conf configs/yolov6s.py \ # configs/yolov6t.py
									--data data/coco.yaml \
									--epoch 400 \
									--device 0,1,2,3,4,5,6,7 \
									--name yolov6s_coco # yolov6t_coco

For m/l model

# Step 1: Training a base model
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \
									--batch 256 \
									--conf configs/yolov6m.py \ # configs/yolov6l.py
									--data data/coco.yaml \
									--epoch 300 \
									--device 0,1,2,3,4,5,6,7 \
									--name yolov6m_coco # yolov6l_coco
									
                                                                                      
# Step 2: Self-distillation training
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \
									--batch 256 \ # 128 for distillation of yolov6l 
									--conf configs/yolov6m.py \ # configs/yolov6l.py
									--data data/coco.yaml \
									--epoch 300 \
									--device 0,1,2,3,4,5,6,7 \
									--distill \
									--teacher_model_path runs/train/yolov6m_coco/weights/best_ckpt.pt \ # # yolov6l_coco
									--name yolov6m_coco # yolov6l_coco
							
  • conf: select config file to specify network/optimizer/hyperparameters
  • data: prepare COCO dataset, YOLO format coco labels and specify dataset paths in data.yaml
  • make sure your dataset structure as follows:
β”œβ”€β”€ coco
β”‚   β”œβ”€β”€ annotations
β”‚   β”‚   β”œβ”€β”€ instances_train2017.json
β”‚   β”‚   └── instances_val2017.json
β”‚   β”œβ”€β”€ images
β”‚   β”‚   β”œβ”€β”€ train2017
β”‚   β”‚   └── val2017
β”‚   β”œβ”€β”€ labels
β”‚   β”‚   β”œβ”€β”€ train2017
β”‚   β”‚   β”œβ”€β”€ val2017
β”‚   β”œβ”€β”€ LICENSE
β”‚   β”œβ”€β”€ README.txt

Evaluation

Reproduce mAP on COCO val2017 dataset with 640Γ—640 resolution

python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s.pt --task val --reproduce_640_eval
Resume training

If your training process is corrupted, you can resume training by

# multi GPU training.
python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --resume

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

Deployment

Tutorials

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 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-RepOpt 640 43.3300e 596int8 869int8 17.2 44.2
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
Legacy models
Model Size mAPval
0.5:0.95
SpeedV100
fp16 b32
(ms)
SpeedV100
fp32 b32
(ms)
SpeedT4
trt fp16 b1
(fps)
SpeedT4
trt fp16 b32
(fps)
Params
(M)
FLOPs
(G)
YOLOv6-N 416
640
30.8
35.0
0.3
0.5
0.4
0.7
1100
788
2716
1242
4.3
4.3
4.7
11.1
YOLOv6-T 640 41.3 0.9 1.5 425 602 15.0 36.7
YOLOv6-S 640 43.1 1.0 1.7 373 520 17.2 44.2
  • Results of the mAP and speed are evaluated on COCO val2017 dataset with the input resolution of 640Γ—640.
  • Refer to Test speed tutorial to reproduce the speed results of YOLOv6.
  • Params and FLOPs of YOLOv6 are estimated on deployed models.
  • For N/T/S models, we use more training epochs strategy.
  • For M/L/L-ReLU models, we adopt self-distillation methods to further improve the performance.

Third-party resources

FAQ(Continuously updatedοΌ‰

About

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

License:GNU General Public License v3.0


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