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English | [ç®ä½ä¸æ](README_cn.md)
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<a href="https://colab.research.google.com/github/meituan/YOLOv6/blob/main/turtorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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## YOLOv6
Implementation of paper:
- [YOLOv6 v3.0: A Full-Scale Reloading](https://arxiv.org/abs/2301.05586) ð¥
- [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" />
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## What's New
- [2023.04.28] Release [YOLOv6Lite](configs/yolov6_lite/README.md) models on mobile or CPU. âï¸ [Mobile Benchmark](#Mobile-Benchmark)
- [2023.03.10] Release [YOLOv6-Face](https://github.com/meituan/YOLOv6/tree/yolov6-face). ð¥ [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. âï¸ [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. ð [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/0.4.0/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 |
<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 640Ã640 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<sup>300e</sup><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<sup>300e</sup><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<sup>300e</sup><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 ð
| 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
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温馨提示
使用train.py文件训练网络,对于训练后的网络,使用infer.py可以得到网络输出的预测结果。通过改变myself.yaml文件可以实现训练自己的数据集,由于内存有限,未上传原始数据集,原始任务为医学图像检测,代码需在GPU上运行,建议使用服务器。预测的结果位于runs文件夹下。 如果有用喜欢的话,欢迎━(*`∀´*)ノ亻!点赞收藏+关注,任何问题都可以直接和我联系,详情可见我的博文: https://blog.csdn.net/qq_52060635/article/details/134149072?spm=1001.2014.3001.5502 YOLOv6 是美团视觉智能部研发的一款目标检测框架,致力于工业应用。论文题目是《YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications》。 本框架同时专注于检测的精度和推理效率,在工业界常用的尺寸模型中:YOLOv6-nano 在 COCO 上精度可达 35.0% AP,在 T4 上推理速度可达 1242 FPS;YOLOv6-
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YoLoV6目标检测代码 (2000个子文件)
ndkcamera.cpp 24KB
yolov6.cpp 15KB
yolo.cpp 12KB
yolox.cpp 7KB
yolov6ncnn.cpp 7KB
yolov5.cpp 6KB
yolov6.cpp 6KB
logging.h 16KB
ndkcamera.h 2KB
yolo.h 2KB
inference.ipynb 4.73MB
MainActivity.java 5KB
Yolov6Ncnn.java 1KB
train_batch.jpg 1.02MB
yoloxs.jpg 526KB
yolov5s.jpg 518KB
yolov6s.jpg 511KB
voc_loss_curve.jpg 326KB
yolov6lite_l_ncnn.jpg 150KB
image3.jpg 136KB
predictions.json 266KB
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.train_cache.json 54KB
instances_val.json 7KB
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README.md 18KB
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