Laughing-q/YOLOv6

YOLOv6

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

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

[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.⭐️ Benchmark
[2022.06.23] Release N/T/S models with excellent performance.

Benchmark

Model	Size	mAP^val 0.5:0.95	Speed^{T4 trt fp16 b1 (fps)}	Speed^{T4 trt fp16 b32 (fps)}	Params ^(M)	FLOPs ^(G)
YOLOv6-N	640	35.9^300e 36.3^400e	802	1234	4.3	11.1
YOLOv6-T	640	40.3^300e 41.1^400e	449	659	15.0	36.7
YOLOv6-S	640	43.5^300e 43.8^400e	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	mAP^val 0.5:0.95	Speed^{T4 trt b1 (fps)}	Speed^{T4 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.
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.

Legacy models

Model	Size	mAP^val 0.5:0.95	Speed^{V100 fp16 b32 (ms)}	Speed^{V100 fp32 b32 (ms)}	Speed^{T4 trt fp16 b1 (fps)}	Speed^{T4 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

Quick Start

Install

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

Training

Single GPU

python tools/train.py --batch 32 --conf configs/yolov6s_finetune.py --data data/dataset.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_finetune.py --data data/dataset.yaml --device 0,1,2,3,4,5,6,7

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 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

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

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 resolution ⭐️

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

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

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

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 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

Laughing-q / YOLOv6

YOLOv6

What's New

Benchmark

Quantized model 🚀

Quick Start

FAQ（Continuously updated）

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