yolov6摩托车佩戴头盔和驾驶员检测+训练好的权重

<p align="center"> <img src="assets/banner-YOLO.png" align="middle" width = "1000" /> </p> English | [简体中文](README_cn.md) <br> <div> </a> <a href="[https://colab.research.google.com/github/meituan/YOLOv6/blob/main/turtorial.ipynb](https://colab.research.google.com/gist/HouSanDuo123/bdad4ad6706209d4e9275d2186d54289/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/code/housanduo/yolov6"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> </div> <br> ## YOLOv6 Implementation of paper - [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://arxiv.org/abs/2209.02976) <p align="center"> <img src="assets/speed_comparision_v3.png" align="middle" width = "1000" /> </p> ## What's New - [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 | mAP<sup>val<br/>0.5:0.95 | Speed<sup>T4<br/>trt fp16 b1 <br/>(fps) | Speed<sup>T4<br/>trt fp16 b32 <br/>(fps) | Params<br/><sup> (M) | FLOPs<br/><sup> (G) | | :----------------------------------------------------------- | ---- | :----------------------- | --------------------------------------- | ---------------------------------------- | -------------------- | ------------------- | | [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6n.pt) | 640 | 37.5 | 779 | 1187 | 4.7 | 11.4 | | [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6s.pt) | 640 | 45.0 | 339 | 484 | 18.5 | 45.3 | | [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6m.pt) | 640 | 50.0 | 175 | 226 | 34.9 | 85.8 | | [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6l.pt) | 640 | 52.8 | 98 | 116 | 59.6 | 150.7 | | | | | | | | [**YOLOv6-N6**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6n6.pt) | 1280 | 44.9 | 228 | 281 | 10.4 | 49.8 | | [**YOLOv6-S6**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6s6.pt) | 1280 | 50.3 | 98 | 108 | 41.4 | 198.0 | | [**YOLOv6-M6**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6m6.pt) | 1280 | 55.2 | 47 | 55 | 79.6 | 379.5 | | [**YOLOv6-L6**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6l6.pt) | 1280 | 57.2 | 26 | 29 | 140.4 | 673.4 | <details> <summary>Table Notes</summary> - 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 640×640 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. </details> <details> <summary>Legacy models</summary> | Model | Size | mAP<sup>val<br/>0.5:0.95 | Speed<sup>T4<br/>trt fp16 b1 <br/>(fps) | Speed<sup>T4<br/>trt fp16 b32 <br/>(fps) | Params<br/><sup> (M) | FLOPs<br/><sup> (G) | | :----------------------------------------------------------- | ---- | :------------------------------------ | --------------------------------------- | ---------------------------------------- | -------------------- | ------------------- | | [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6n.pt) | 640 | 35.9^300e<br/>36.3<sup>400e | 802 | 1234 | 4.3 | 11.1 | | [**YOLOv6-T**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6t.pt) | 640 | 40.3^300e<br/>41.1<sup>400e | 449 | 659 | 15.0 | 36.7 | | [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6s.pt) | 640 | 43.5^300e<br/>43.8<sup>400e | 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 | mAP<sup>val<br/>0.5:0.95 | Speed<sup>T4<br/>trt b1 <br/>(fps) | Speed<sup>T4<br/>trt b32 <br/>(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 fig