目标检测-yolov6源码

# MT-YOLOv6 [About Naming YOLOv6](./docs/About_naming_yolov6.md) ## Introduction YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance. <img src="assets/picture.png" width="800"> YOLOv6-nano achieves 35.0 mAP on COCO val2017 dataset with 1242 FPS on T4 using TensorRT FP16 for bs32 inference, and YOLOv6-s achieves 43.1 mAP on COCO val2017 dataset with 520 FPS on T4 using TensorRT FP16 for bs32 inference. YOLOv6 is composed of the following methods: - Hardware-friendly Design for Backbone and Neck - Efficient Decoupled Head with SIoU Loss ## Coming soon - [ ] YOLOv6 m/l/x model. - [ ] Deployment for MNN/TNN/NCNN/CoreML... - [ ] Quantization tools ## Quick Start ### Install ```shell git clone https://github.com/meituan/YOLOv6 cd YOLOv6 pip install -r requirements.txt ``` ### Inference First, download a pretrained model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases/tag/0.1.0) Second, run inference with `tools/infer.py` ```shell python tools/infer.py --weights yolov6s.pt --source img.jpg / imgdir yolov6n.pt ``` ### Training Single GPU ```shell python tools/train.py --batch 32 --conf configs/yolov6s.py --data data/coco.yaml --device 0 configs/yolov6n.py ``` Multi GPUs (DDP mode recommended) ```shell 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 configs/yolov6n.py ``` - conf: select config file to specify network/optimizer/hyperparameters - data: prepare [COCO](http://cocodataset.org) dataset and specify dataset paths in data.yaml ### Evaluation Reproduce mAP on COCO val2017 dataset ```shell python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s.pt --task val yolov6n.pt ``` ### Deployment * [ONNX](./deploy/ONNX) * [OpenVINO](./deploy/OpenVINO) ### Tutorials * [Train custom data](./docs/Train_custom_data.md) * [Test speed](./docs/Test_speed.md) ## Benchmark | Model | Size | mAP<sup>val<br/>0.5:0.95 | Speed<sup>V100<br/>fp16 b32 <br/>(ms) | Speed<sup>V100<br/>fp32 b32 <br/>(ms) | 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/0.1.0/yolov6n.pt) | 416<br/>640 | 30.8<br/>35.0 | 0.3<br/>0.5 | 0.4<br/>0.7 | 1100<br/>788 | 2716<br/>1242 | 4.3<br/>4.3 | 4.7<br/>11.1 | | [**YOLOv6-tiny**](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6t.pt) | 640 | 41.3 | 0.9 | 1.5 | 425 | 602 | 15.0 | 36.7 | | [**YOLOv6-s**](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6s.pt) | 640 | 43.1 | 1.0 | 1.7 | 373 | 520 | 17.2 | 44.2 | - Comparisons of the mAP and speed of different object detectors are tested on [COCO val2017](https://cocodataset.org/#download) dataset. - 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 model. - Speed results of other methods are tested in our environment using official codebase and model if not found from the corresponding official release. ## Third-party resources * 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-python/blob/main/yolov6/trt.py) 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)