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Chilicyy committed Mar 10, 2023
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12 changes: 6 additions & 6 deletions README.md
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Expand Up @@ -19,10 +19,10 @@ New Feature
## Performance on WIDERFACE
| Model | Size | Easy | Medium | Hard | 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_face.pt) | 640 | 95.0 | 92.4 | 80.4 | 797 | 1313 | 4.63 | 11.35 |
| [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6s_face.pt) | 640 | 96.2 | 94.7 | 85.1 | 339 | 484 | 12.41 | 32.45 |
| [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6m_face.pt) | 640 | 97.0 | 95.3 | 86.3 | 188 | 240 | 24.85 | 70.59 |
| [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6l_face.pt) | 640 | 97.2 | 95.9 | 87.5 | 102 | 121 | 56.77 | 159.24 |
| [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.3.1/yolov6n_face.pt) | 640 | 95.0 | 92.4 | 80.4 | 797 | 1313 | 4.63 | 11.35 |
| [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.3.1/yolov6s_face.pt) | 640 | 96.2 | 94.7 | 85.1 | 339 | 484 | 12.41 | 32.45 |
| [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.3.1/yolov6m_face.pt) | 640 | 97.0 | 95.3 | 86.3 | 188 | 240 | 24.85 | 70.59 |
| [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.3.1/yolov6l_face.pt) | 640 | 97.2 | 95.9 | 87.5 | 102 | 121 | 56.77 | 159.24 |

#### Table Notes

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```
- fuse_ab: Anchor Aided Training Mode
- conf: select config file to specify network/optimizer/hyperparameters. We recommend to apply yolov6n/s/m/l_finetune.py when training on WIDER FACE or your custom dataset.
- data: prepare dataset and specify dataset paths in data.yaml ( [WIDERFACE](http://shuoyang1213.me/WIDERFACE/), [YOLO format widerface labels](https://github.com/meituan/YOLOv6/releases/download/0.3.0/widerface_yololabels.zip) )
- data: prepare dataset and specify dataset paths in data.yaml ( [WIDERFACE](http://shuoyang1213.me/WIDERFACE/), [YOLO format widerface labels](https://github.com/meituan/YOLOv6/releases/download/0.3.1/widerface_yololabels.zip) )
- make sure your dataset structure as follows:
```
├── widerface
Expand All @@ -81,7 +81,7 @@ python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 64
<details open>
<summary>Inference</summary>

First, download a pretrained model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases/tag/0.3.0) or use your trained model to do inference.
First, download a pretrained model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases/tag/0.3.1) or use your trained model to do inference.

Second, run inference with `tools/infer.py`

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12 changes: 6 additions & 6 deletions README_cn.md
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## WIDERFACE 模型指标
| Model | Size | Easy | Medium | Hard | 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_face.pt) | 640 | 95.0 | 92.4 | 80.4 | 797 | 1313 | 4.63 | 11.35 |
| [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6s_face.pt) | 640 | 96.2 | 94.7 | 85.1 | 339 | 484 | 12.41 | 32.45 |
| [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6m_face.pt) | 640 | 97.0 | 95.3 | 86.3 | 188 | 240 | 24.85 | 70.59 |
| [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.3.0/yolov6l_face.pt) | 640 | 97.2 | 95.9 | 87.5 | 102 | 121 | 56.77 | 159.24 |
| [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.3.1/yolov6n_face.pt) | 640 | 95.0 | 92.4 | 80.4 | 797 | 1313 | 4.63 | 11.35 |
| [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.3.1/yolov6s_face.pt) | 640 | 96.2 | 94.7 | 85.1 | 339 | 484 | 12.41 | 32.45 |
| [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.3.1/yolov6m_face.pt) | 640 | 97.0 | 95.3 | 86.3 | 188 | 240 | 24.85 | 70.59 |
| [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.3.1/yolov6l_face.pt) | 640 | 97.2 | 95.9 | 87.5 | 102 | 121 | 56.77 | 159.24 |

#### 表格备注

Expand Down Expand Up @@ -62,7 +62,7 @@ python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 64
```
- fuse_ab: 使用联合锚点训练模式
- 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 WIDERFACE 数据集为例,您可以在 [WIDERFACE](http://shuoyang1213.me/WIDERFACE/) 下载数据, 在这里下载[YOLO 格式标签](https://github.com/meituan/YOLOv6/releases/download/0.3.0/widerface_yololabels.zip)
- data: 数据集配置文件,以 WIDERFACE 数据集为例,您可以在 [WIDERFACE](http://shuoyang1213.me/WIDERFACE/) 下载数据, 在这里下载[YOLO 格式标签](https://github.com/meituan/YOLOv6/releases/download/0.3.1/widerface_yololabels.zip)
- 确保您的数据集按照下面这种格式来组织;
```
├── widerface
Expand All @@ -80,7 +80,7 @@ python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 64
<details open>
<summary>推理</summary>

首先,从[release 页面](https://github.com/meituan/YOLOv6/releases/tag/0.3.0) 下载一个训练好的模型权重文件,或选择您自己训练的模型;
首先,从[release 页面](https://github.com/meituan/YOLOv6/releases/tag/0.3.1) 下载一个训练好的模型权重文件,或选择您自己训练的模型;

然后,通过 `tools/infer.py` 文件进行推理。

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