214-yolov6-a-single-stage-object-detection-framework-for-industrial-applications
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Citation
https://github.com/SZU-AdvTech-2024/214-YOLOv6-A-Single-Stage-Object-Detection-Framework-for-Industrial-Applications/blob/main/
English | [](README_cn.md)
## YOLOv6 Implementation of paper: - [YOLOv6 v3.0: A Full-Scale Reloading](https://arxiv.org/abs/2301.05586) - [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://arxiv.org/abs/2209.02976)## What's New - [2023.09.15] Release [YOLOv6-Segmentation](https://github.com/meituan/YOLOv6/tree/yolov6-seg). [Performance](https://github.com/meituan/YOLOv6/tree/yolov6-seg#yolov6-segmentation) - [2023.04.28] Release [YOLOv6Lite](configs/yolov6_lite/README.md) models on mobile or CPU. [Mobile Benchmark](#Mobile-Benchmark) - [2023.03.10] Release [YOLOv6-Face](https://github.com/meituan/YOLOv6/tree/yolov6-face). [Performance](https://github.com/meituan/YOLOv6/tree/yolov6-face#performance-on-widerface) - [2023.03.02] Update [base models](configs/base/README.md) to version 3.0. - [2023.01.06] Release P6 models and enhance the performance of P5 models. [Benchmark](#Benchmark) - [2022.11.04] Release [base models](configs/base/README.md) to simplify the training and deployment process. - [2022.09.06] Customized quantization methods. [Quantization Tutorial](./tools/qat/README.md) - [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**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6n.pt) | 640 | 37.5 | 779 | 1187 | 4.7 | 11.4 | | [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6s.pt) | 640 | 45.0 | 339 | 484 | 18.5 | 45.3 | | [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6m.pt) | 640 | 50.0 | 175 | 226 | 34.9 | 85.8 | | [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6l.pt) | 640 | 52.8 | 98 | 116 | 59.6 | 150.7 | | | | | | | | [**YOLOv6-N6**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6n6.pt) | 1280 | 44.9 | 228 | 281 | 10.4 | 49.8 | | [**YOLOv6-S6**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6s6.pt) | 1280 | 50.3 | 98 | 108 | 41.4 | 198.0 | | [**YOLOv6-M6**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6m6.pt) | 1280 | 55.2 | 47 | 55 | 79.6 | 379.5 | | [**YOLOv6-L6**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6l6.pt) | 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](https://cocodataset.org/#download) dataset with the input resolution of 640640 for P5 models and 1280x1280 for P6 models. - Speed is tested with TensorRT 7.2 on T4. - Refer to [Test speed](./docs/Test_speed.md) tutorial to reproduce the speed results of YOLOv6. - Params and FLOPs of YOLOv6 are estimated on deployed models.## Mobile Benchmark | Model | Size | mAPvalLegacy models
| Model | Size | mAPval
0.5:0.95 | SpeedT4
trt fp16 b1
(fps) | SpeedT4
trt fp16 b32
(fps) | Params
(M) | FLOPs
(G) | | :----------------------------------------------------------- | ---- | :------------------------------------ | --------------------------------------- | ---------------------------------------- | -------------------- | ------------------- | | [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6n.pt) | 640 | 35.9300e
36.3400e | 802 | 1234 | 4.3 | 11.1 | | [**YOLOv6-T**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6t.pt) | 640 | 40.3300e
41.1400e | 449 | 659 | 15.0 | 36.7 | | [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6s.pt) | 640 | 43.5300e
43.8400e | 358 | 495 | 17.2 | 44.2 | | [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6m.pt) | 640 | 49.5 | 179 | 233 | 34.3 | 82.2 | | [**YOLOv6-L-ReLU**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6l_relu.pt) | 640 | 51.7 | 113 | 149 | 58.5 | 144.0 | | [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6l.pt) | 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.
0.5:0.95 | sm8350
(ms) | mt6853
(ms) | sdm660
(ms) |Params
(M) | FLOPs
(G) | | :----------------------------------------------------------- | ---- | -------------------- | -------------------- | -------------------- | -------------------- | -------------------- | -------------------- | | [**YOLOv6Lite-S**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6lite_s.pt) | 320*320 | 22.4 | 7.99 | 11.99 | 41.86 | 0.55 | 0.56 | | [**YOLOv6Lite-M**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6lite_m.pt) | 320*320 | 25.1 | 9.08 | 13.27 | 47.95 | 0.79 | 0.67 | | [**YOLOv6Lite-L**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6lite_l.pt) | 320*320 | 28.0 | 11.37 | 16.20 | 61.40 | 1.09 | 0.87 | | [**YOLOv6Lite-L**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6lite_l.pt) | 320*192 | 25.0 | 7.02 | 9.66 | 36.13 | 1.09 | 0.52 | | [**YOLOv6Lite-L**](https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6lite_l.pt) | 224*128 | 18.9 | 3.63 | 4.99 | 17.76 | 1.09 | 0.24 |## Quick StartTable 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](https://cocodataset.org/#download) 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](./docs/Test_NCNN_speed.md) tutorial to reproduce the NCNN speed results of YOLOv6Lite.Install
```shell 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](./docs/Train_coco_data.md).Finetune on custom data
Single GPU ```shell # 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) ```shell # 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](http://cocodataset.org), [YOLO format coco labels](https://github.com/meituan/YOLOv6/releases/download/0.1.0/coco2017labels.zip) ) - 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](./docs/About_training_size.md).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 640640 or 1280x1280 resolution ```shell # 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](configs/experiment/yolov6n_with_eval_params.py)Inference
First, download a pretrained model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases/tag/0.4.0) or use your trained model to do inference. Second, run inference with `tools/infer.py` ```shell # 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: ```shell # 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](./deploy/ONNX) * [OpenCV Python/C++](./deploy/ONNX/OpenCV) * [OpenVINO](./deploy/OpenVINO) * [TensorRT](./deploy/TensorRT) * [NCNN](./deploy/NCNN) * [Android](./deploy/NCNN/Android)Tutorials
* [User Guide(zh_CN)](https://yolov6-docs.readthedocs.io/zh_CN/latest/) * [Train COCO Dataset](./docs/Train_coco_data.md) * [Train custom data](./docs/Train_custom_data.md) * [Test speed](./docs/Test_speed.md) * [Tutorial of Quantization for YOLOv6](./docs/Tutorial%20of%20Quantization.md)### [FAQContinuously updated](https://github.com/meituan/YOLOv6/wiki/FAQ%EF%BC%88Continuously-updated%EF%BC%89) If you have any questions, welcome to join our WeChat group to discuss and exchange.Third-party resources
* YOLOv6 Training with Amazon Sagemaker: [yolov6-sagemaker](https://github.com/ashwincc/yolov6-sagemaker) from [ashwincc](https://github.com/ashwincc) * YOLOv6 NCNN Android app demo: [ncnn-android-yolov6](https://github.com/FeiGeChuanShu/ncnn-android-yolov6) from [FeiGeChuanShu](https://github.com/FeiGeChuanShu) * YOLOv6 ONNXRuntime/MNN/TNN C++: [YOLOv6-ORT](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ort/cv/yolov6.cpp), [YOLOv6-MNN](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/mnn/cv/mnn_yolov6.cpp) and [YOLOv6-TNN](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_yolov6.cpp) from [DefTruth](https://github.com/DefTruth) * YOLOv6 TensorRT Python: [yolov6-tensorrt-python](https://github.com/Linaom1214/TensorRT-For-YOLO-Series) from [Linaom1214](https://github.com/Linaom1214) * YOLOv6 TensorRT Windows C++: [yolort](https://github.com/zhiqwang/yolov5-rt-stack/tree/main/deployment/tensorrt-yolov6) from [Wei Zeng](https://github.com/Wulingtian) * [YOLOv6 web demo](https://huggingface.co/spaces/nateraw/yolov6) on [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). [](https://huggingface.co/spaces/nateraw/yolov6) * [Interactive demo](https://yolov6.dagshubusercontent.com/) on [DagsHub](https://dagshub.com) with [Streamlit](https://github.com/streamlit/streamlit) * Tutorial: [How to train YOLOv6 on a custom dataset](https://blog.roboflow.com/how-to-train-yolov6-on-a-custom-dataset/)
Owner
- Name: SZU-AdvTech-2024
- Login: SZU-AdvTech-2024
- Kind: organization
- Repositories: 1
- Profile: https://github.com/SZU-AdvTech-2024
Citation (citation.txt)
@misc{REPO214,
author = "Li, Chuyi and Li, Lulu and Jiang, Hongliang and Weng, Kaiheng and Geng, Yifei and Li, Liang and Ke, Zaidan and Li, Qingyuan and Cheng, Meng and Nie, Weiqiang and Li, Yiduo and Zhang, Bo and Liang, Yufei and Zhou, Linyuan and Xu, Xiaoming and Chu, Xiangxiang and Wei, Xiaoming and Wei, Xiaolin",
archiveprefix = "arXiv",
eprint = "2209.02976",
title = "{YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications}",
year = "2022"
}
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