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# 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.
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