YOLOv6

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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. 🚀 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

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

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

# 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:

├── coco
│   ├── annotations
│   │   ├── instances_train2017.json
│   │   └── instances_val2017.json
│   ├── images
│   │   ├── train2017
│   │   └── val2017
│   ├── labels
│   │   ├── train2017
│   │   ├── val2017
│   ├── LICENSE
│   ├── README.txt

Evaluation

Reproduce mAP on COCO val2017 dataset with 640×640 resolution

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

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

# 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

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

Tutorials

• Train custom data
• Test speed
• Tutorial of Quantization for YOLOv6

Benchmark

• Results of the mAP and speed are evaluated on COCO val2017 dataset with the input resolution of 640×640.
• 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

• 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
• Demo of YOLOv6 inference on Google Colab

FAQ（Continuously updated）