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Chilicyy committed Sep 15, 2023
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35 changes: 16 additions & 19 deletions README.md
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Expand Up @@ -9,26 +9,21 @@ English | [简体中文](README_cn.md)

Implementation of Instance Segmentation based on [YOLOv6 v4.0 code](https://github.com/meituan/YOLOv6/tree/main).

New Feature
- Designe two types of segment heads referring to YOLACT and SOLO
<p align="left">
<img src="assets/speed_comparision_seg.png" align="center", width="500"/>
<br>
</p>

## Performance on MSCOCO
| Model | Size | mAP<sup>box<br/>50-95 | mAP<sup>mask<br/>50-95 | Speed<sup>T4<br/>trt fp16 b1 <br/>(fps) |
| :----------------------------------------------------------- | ---- | :-------------------- | ---------------------- | --------------------------------------- |
| [**YOLOv6-N-seg**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6n_seg.pt) | 640 | 35.3 | 31.2 | 645 |
| [**YOLOv6-S-seg**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6s_seg.pt) | 640 | 44.0 | 38.0 | 292 |
| [**YOLOv6-M-seg**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6m_seg.pt) | 640 | 48.2 | 41.3 | 148 |
| [**YOLOv6-L-seg**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6l_seg.pt) | 640 | 51.1 | 43.7 | 93 |
| [**YOLOv6-X-seg**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6x_seg.pt) | 640 | 52.2 | 44.8 | 47 |
| | | | | |
| [**YOLOv6-N-solo**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6n_solo.pt) | 640 | 35.7 | 31.3 | 506 |
| [**YOLOv6-S-solo**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6s_solo.pt) | 640 | 44.2 | 39.0 | 243 |
| [**YOLOv6-M-solo**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6m_solo.pt) | 640 | 48.3 | 42.2 | 120 |
| [**YOLOv6-L-solo**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6l_solo.pt) | 640 | 50.9 | 44.4 | 75 |
| [**YOLOv6-X-solo**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6x_solo.pt) | 640 | 52.2 | 45.0 | 41 |
| Model | Size | mAP<sup>box<br/>50-95 | mAP<sup>mask<br/>50-95 | Speed<sup>T4<br/>trt fp16 b1 <br/>(fps) | Params<br/><sup> (M) | FLOPs<br/><sup> (G) |
| :----------------------------------------------------------- | ---- | :-------------------- | ---------------------- | --------------------------------------- | -------------------- | ------------------- |
| [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6n_seg.pt) | 640 | 35.3 | 31.2 | 645 | 4.9 | 7.0 |
| [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6s_seg.pt) | 640 | 44.0 | 38.0 | 292 | 19.6 | 27.7 |
| [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6m_seg.pt) | 640 | 48.2 | 41.3 | 148 | 37.1 | 54.3 |
| [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6l_seg.pt) | 640 | 51.1 | 43.7 | 93 | 63.6 | 95.5 |
| [**YOLOv6-X**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6x_seg.pt) | 640 | 52.2 | 44.8 | 47 | 119.1 | 175.5 |

#### Table Notes
- YOLOv6-x-seg models apply YOLACT segment head, and YOLOv6-x-solo models apply SOLO segment head.
- All checkpoints are trained from scratch on COCO for 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.
- Speed is tested with TensorRT 8.5 on T4 without post-processing.
Expand All @@ -45,7 +40,7 @@ New Feature
```shell
git clone https://github.com/meituan/YOLOv6
cd YOLOv6
git checkout yolov6-segss
git checkout yolov6-seg
pip install -r requirements.txt
```
</details>
Expand All @@ -66,7 +61,7 @@ python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 64
```
- fuse_ab: Not supported in current version
- 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) )
- 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/0.4.1/coco2017labels-segments.zip) )
- make sure your dataset structure as follows:
```
├── coco
Expand All @@ -83,7 +78,6 @@ python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 64
│ ├── README.txt
```

YOLOv6 supports different input resolution modes. For details, see [How to Set the Input Size](./docs/About_training_size.md).

</details>

Expand All @@ -96,6 +90,7 @@ Reproduce mAP on COCO val2017 dataset with 640×640 resolution
```shell
python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s.pt --task val
```

</details>


Expand All @@ -114,6 +109,8 @@ If you want to inference on local camera or web camera, you can run:
python tools/infer.py --weights yolov6s.pt --webcam --webcam-addr 0
```
`webcam-addr` can be local camera number id or rtsp address.
Maybe you want to eval a solo-head model, remember to add the *--issolo* parameter.

</details>

<details>
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38 changes: 16 additions & 22 deletions README_cn.md
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Expand Up @@ -8,42 +8,37 @@

基于 [YOLOv6 v4.0 分支](https://github.com/meituan/YOLOv6/tree/main) 代码实现实例分割任务。

新特性
- 参考 YOLACT 和 SOLO 设计了两种不同的分割预测头
<p align="left">
<img src="assets/speed_comparision_seg.png" align="center", width="500"/>
<br>
</p>

## MSCOCO 模型指标
| Model | Size | mAP<sup>box<br/>50-95 | mAP<sup>mask<br/>50-95 | Speed<sup>T4<br/>trt fp16 b1 <br/>(fps) |
| :----------------------------------------------------------- | ---- | :-------------------- | ---------------------- | --------------------------------------- |
| [**YOLOv6-N-seg**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6n_seg.pt) | 640 | 35.3 | 31.2 | 645 |
| [**YOLOv6-S-seg**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6s_seg.pt) | 640 | 44.0 | 38.0 | 292 |
| [**YOLOv6-M-seg**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6m_seg.pt) | 640 | 48.2 | 41.3 | 148 |
| [**YOLOv6-L-seg**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6l_seg.pt) | 640 | 51.1 | 43.7 | 93 |
| [**YOLOv6-X-seg**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6x_seg.pt) | 640 | 52.2 | 44.8 | 47 |
| | | | | |
| [**YOLOv6-N-solo**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6n_solo.pt) | 640 | 35.7 | 31.3 | 506 |
| [**YOLOv6-S-solo**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6s_solo.pt) | 640 | 44.2 | 39.0 | 243 |
| [**YOLOv6-M-solo**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6m_solo.pt) | 640 | 48.3 | 42.2 | 120 |
| [**YOLOv6-L-solo**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6l_solo.pt) | 640 | 50.9 | 44.4 | 75 |
| [**YOLOv6-X-solo**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6x_solo.pt) | 640 | 52.2 | 45.0 | 41 |

#### Table Notes
- YOLOv6-x-seg 模型采用 YOLACT 分割头, YOLOv6-x-solo 模型采用 SOLO 分割头.
| Model | Size | mAP<sup>box<br/>50-95 | mAP<sup>mask<br/>50-95 | Speed<sup>T4<br/>trt fp16 b1 <br/>(fps) | Params<br/><sup> (M) | FLOPs<br/><sup> (G) |
| :----------------------------------------------------------- | ---- | :-------------------- | ---------------------- | --------------------------------------- | -------------------- | ------------------- |
| [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6n_seg.pt) | 640 | 35.3 | 31.2 | 645 | 4.9 | 7.0 |
| [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6s_seg.pt) | 640 | 44.0 | 38.0 | 292 | 19.6 | 27.7 |
| [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6m_seg.pt) | 640 | 48.2 | 41.3 | 148 | 37.1 | 54.3 |
| [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6l_seg.pt) | 640 | 51.1 | 43.7 | 93 | 63.6 | 95.5 |
| [**YOLOv6-X**](https://github.com/meituan/YOLOv6/releases/download/0.4.1/yolov6x_seg.pt) | 640 | 52.2 | 44.8 | 47 | 119.1 | 175.5 |

#### 表格笔记
- 所有权重都经过 300 个 epoch 的训练，并且没有使用蒸馏技术.
- mAP 和速度指标是在 COCO val2017 数据集上评估的，输入分辨率为640x640.
- 速度是在 T4 上测试的，TensorRT 版本为 8.5，且不包括后处理部分.
- YOLOv6 的参数和计算量是在推理模式下计算的.



## Quick Start
## 快速开启
<details open>
<summary> 安装 </summary>


```shell
git clone https://github.com/meituan/YOLOv6
cd YOLOv6
git checkout yolov6-segss
git checkout yolov6-seg
pip install -r requirements.txt
```
</details>
Expand All @@ -64,7 +59,7 @@ python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 64
```
- fuse_ab: 当前版本暂不支持
- conf: 配置文件路径，里面包含网络结构、优化器配置、超参数信息。如果您是在自己的数据集训练，我们推荐您使用yolov6n/s/m/l_finetune.py配置文件
- data: 数据集配置文件，以 COCO 数据集为例，您可以在 [COCO](http://cocodataset.org) 下载数据, 在这里下载 [YOLO 格式标签](https://github.com/meituan/YOLOv6/releases/download/0.1.0/coco2017labels.zip)；
- data: 数据集配置文件，以 COCO 数据集为例，您可以在 [COCO](http://cocodataset.org) 下载数据, 在这里下载 [YOLO 格式标签](https://github.com/meituan/YOLOv6/releases/download/0.4.1/coco2017labels-segments.zip)；
- 确保您的数据集按照下面这种格式来组织；
```
├── coco
Expand All @@ -81,7 +76,6 @@ python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 64
│ ├── README.txt
```

YOLOv6 支持不同的输入分辨率模式，详情请参见 [如何设置输入大小](./docs/About_training_size_cn.md).

</details>

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