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docs(readme): update training/eval/predict results
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77 changes: 38 additions & 39 deletions README.md
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Expand Up @@ -51,18 +51,18 @@
<tr>
<td class="tg-y5w1">zjykzj/YOLO5Face<span style="font-weight:700;font-style:normal">(This)</span></td>
<td class="tg-zkss"><span style="background-color:#FFF">yolov5s-face</span></td>
<td class="tg-vc3l">15.1</td>
<td class="tg-9y4h">94.34</td>
<td class="tg-c3ow">92.72</td>
<td class="tg-c3ow">84.47</td>
<td class="tg-vc3l">15.2</td>
<td class="tg-9y4h">94.69</td>
<td class="tg-c3ow">93.00</td>
<td class="tg-c3ow">84.73</td>
</tr>
<tr>
<td class="tg-y5w1">zjykzj/YOLO5Face<span style="font-weight:700;font-style:normal">(This)</span></td>
<td class="tg-zkss"><span style="background-color:#FFF">yolov5s</span></td>
<td class="tg-zkss"><span style="background-color:#FFF">yolov5s-v7.0</span></td>
<td class="tg-vc3l">15.8</td>
<td class="tg-zkss">95.21</td>
<td class="tg-vc3l">93.42</td>
<td class="tg-vc3l">84.03</td>
<td class="tg-zkss">94.84</td>
<td class="tg-vc3l">93.28</td>
<td class="tg-vc3l">84.67</td>
</tr>
</tbody></table>

Expand All @@ -73,7 +73,6 @@
- [Background](#background)
- [Installation](#installation)
- [Usage](#usage)
- [Data](#data)
- [Train](#train)
- [Eval](#eval)
- [Predict](#predict)
Expand All @@ -84,13 +83,20 @@

## Latest News

* ***[2024/06/29][v0.1.0](https://github.com/zjykzj/YOLO5Face/releases/tag/v0.1.0). Training Face Detectors Based on YOLOv5-v7.0 and WIDERFACE Datasets.***
* ***[2024/06/29][v0.1.0](https://github.com/zjykzj/YOLO5Face/releases/tag/v0.1.0). Training Face Detectors Based on
YOLOv5-v7.0 and WIDERFACE Datasets.***

## Background

[YOLO5Face](https://arxiv.org/abs/2105.12931) is a very interesting work that further abstracts the task of face detection. By directly using a universal object detection algorithm, good face detection results can be achieved. Of course, it also achieves 5-point facial keypoint regression. Based on the [ultralytics/yolov5](https://github.com/ultralytics/yolov5) object detection framework, YOLO5Face can easily apply different models and training, such as using lightweight networks for real-time detection and using large networks for higher detection accuracy.
[YOLO5Face](https://arxiv.org/abs/2105.12931) is a very interesting work that further abstracts the task of face
detection. By directly using a universal object detection algorithm, good face detection results can be achieved. Of
course, it also achieves 5-point facial keypoint regression. Based on
the [ultralytics/yolov5](https://github.com/ultralytics/yolov5) object detection framework, YOLO5Face can easily apply
different models and training, such as using lightweight networks for real-time detection and using large networks for
higher detection accuracy.

Note: the latest implementation of YOLO5Face in our warehouse is entirely based on [ultralytics/yolov5 v7.0](https://github.com/ultralytics/yolov5/releases/tag/v7.0)
Note: the latest implementation of YOLO5Face in our warehouse is entirely based
on [ultralytics/yolov5 v7.0](https://github.com/ultralytics/yolov5/releases/tag/v7.0)

## Installation

Expand All @@ -106,51 +112,44 @@ docker run -it --runtime nvidia --gpus=all --shm-size=16g -v /etc/localtime:/etc

## Usage

### Data

Download the WIDERFACE dataset from http://shuoyang1213.me/WIDERFACE/, Then convert WIDERFACE dataset format.

```shell
python3 widerface2yolo.py ../datasets/widerface/WIDER_train/images ../datasets/widerface/wider_face_split/wider_face_train_bbx_gt.txt ../datasets/widerface/
python3 widerface2yolo.py ../datasets/widerface/WIDER_val/images ../datasets/widerface/wider_face_split/wider_face_val_bbx_gt.txt ../datasets/widerface/
```
About dataset and annotations, see [widerface2yolo.py](./widerface2yolo.py)

### Train

```shell
# YOLOv5s
python3 train.py --data widerface.yaml --weights yolov5s.pt --cfg yolov5s.yaml --imgsz 800 --epoch 250 --device 0
# YOLOv5s-face
python3 train.py --data widerface.yaml --weights '' --cfg models/yolo5face/cfgs/yolov5s_face.yaml --hyp models/yolo5face/hyp.scratch.yaml --img 800 --epoch 250 --device 0
# yolov5s_v7.0
$ python3 widerface_train.py --data widerface.yaml --weights "" --cfg models/yolo5face/cfgs/yolov5s_v7_0.yaml --hyp models/yolo5face/hyps/hyp.scratch-low.yaml --img 800 --epoch 300 --device 0
# yolov5s-face
$ python3 widerface_train.py --data widerface.yaml --weights "" --cfg models/yolo5face/cfgs/yolov5s_face.yaml --hyp models/yolo5face/hyps/hyp.scratch.yaml --img 800 --epoch 300 --device 0
```

### Eval

```shell
# python widerface_detect.py --weights ./runs/train/exp4-yolov5s-e250-img800.pt --source ../datasets/widerface/images/val/ --folder_pict ../datasets/widerface/wider_face_split/wider_face_val_bbx_gt.txt --conf-thres 0.001 --iou-thres 0.6 --save-txt --save-conf --device 0
$ python widerface_detect.py --weights ./runs/exp4-yolov5s_v7_0-i800-e300.pt --source ../datasets/widerface/images/val/ --folder_pict ../datasets/widerface/wider_face_split/wider_face_val_bbx_gt.txt --conf-thres 0.001 --iou-thres 0.6 --save-txt --save-conf --device 0
...
YOLOv5s summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs
YOLOv5s_v7_0 summary: 157 layers, 7039792 parameters, 0 gradients, 15.8 GFLOPs
...
Speed: 0.3ms pre-process, 9.0ms inference, 0.9ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp5
0 labels saved to runs/detect/exp5/labels
# cd widerface_evaluate/
# python3 evaluation.py -p ../runs/detect/exp5/labels/ -g ./ground_truth/
Reading Predictions : 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:00<00:00, 94.45it/s]
Processing easy: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:19<00:00, 3.13it/s]
Processing medium: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:19<00:00, 3.12it/s]
Processing hard: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:19<00:00, 3.13it/s]
Speed: 0.4ms pre-process, 8.8ms inference, 0.8ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp7
0 labels saved to runs/detect/exp7/labels
$ cd widerface_evaluate/
$ python3 evaluation.py -p ../runs/detect/exp7/labels/ -g ./ground_truth/
Reading Predictions : 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:00<00:00, 62.18it/s]
Processing easy: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:20<00:00, 2.94it/s]
Processing medium: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:20<00:00, 2.98it/s]
Processing hard: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:20<00:00, 2.97it/s]
==================== Results ====================
Easy Val AP: 0.9520941964576021
Medium Val AP: 0.9341770033021547
Hard Val AP: 0.8403303849682994
Easy Val AP: 0.9483604102331251
Medium Val AP: 0.9328484206418773
Hard Val AP: 0.8467345083774318
=================================================
```

### Predict

```shell
python detect.py --weights ./runs/exp4-yolov5s-e250-img800.pt --source assets/selfie.jpg --imgsz 2048 --conf-thres 0.25 --iou-thres 0.45 --max-det 3000 --hide-labels --hide-conf
$ python detect_face_and_landmarks.py --weights ./runs/exp4-yolov5s_v7_0-i800-e300.pt --source assets/selfie.jpg --imgsz 2048 --conf-thres 0.25 --iou-thres 0.45 --hide-labels --hide-conf
```

![](./assets/results/selfie.jpg)
Expand Down
64 changes: 28 additions & 36 deletions README.zh-CN.md
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Original file line number Diff line number Diff line change
Expand Up @@ -51,18 +51,18 @@
<tr>
<td class="tg-y5w1">zjykzj/YOLO5Face<span style="font-weight:700;font-style:normal">(This)</span></td>
<td class="tg-zkss"><span style="background-color:#FFF">yolov5s-face</span></td>
<td class="tg-vc3l">15.1</td>
<td class="tg-9y4h">94.34</td>
<td class="tg-c3ow">92.72</td>
<td class="tg-c3ow">84.47</td>
<td class="tg-vc3l">15.2</td>
<td class="tg-9y4h">94.69</td>
<td class="tg-c3ow">93.00</td>
<td class="tg-c3ow">84.73</td>
</tr>
<tr>
<td class="tg-y5w1">zjykzj/YOLO5Face<span style="font-weight:700;font-style:normal">(This)</span></td>
<td class="tg-zkss"><span style="background-color:#FFF">yolov5s</span></td>
<td class="tg-zkss"><span style="background-color:#FFF">yolov5s-v7.0</span></td>
<td class="tg-vc3l">15.8</td>
<td class="tg-zkss">95.21</td>
<td class="tg-vc3l">93.42</td>
<td class="tg-vc3l">84.03</td>
<td class="tg-zkss">94.84</td>
<td class="tg-vc3l">93.28</td>
<td class="tg-vc3l">84.67</td>
</tr>
</tbody></table>

Expand All @@ -73,7 +73,6 @@
- [背景](#背景)
- [安装](#安装)
- [用法](#用法)
- [数据](#数据)
- [训练](#训练)
- [评估](#评估)
- [预测](#预测)
Expand Down Expand Up @@ -106,51 +105,44 @@ docker run -it --runtime nvidia --gpus=all --shm-size=16g -v /etc/localtime:/etc

## 用法

### 数据

在[官网](http://shuoyang1213.me/WIDERFACE/)下载WIDERFACE数据集，然后转换数据集格式。

```shell
python3 widerface2yolo.py ../datasets/widerface/WIDER_train/images ../datasets/widerface/wider_face_split/wider_face_train_bbx_gt.txt ../datasets/widerface/
python3 widerface2yolo.py ../datasets/widerface/WIDER_val/images ../datasets/widerface/wider_face_split/wider_face_val_bbx_gt.txt ../datasets/widerface/
```
关于数据集和标注，查看[widerface2yolo.py](./widerface2yolo.py)

### 训练

```shell
# YOLOv5s
python3 train.py --data widerface.yaml --weights yolov5s.pt --cfg yolov5s.yaml --imgsz 800 --epoch 250 --device 0
# YOLOv5s-face
python3 train.py --data widerface.yaml --weights '' --cfg models/yolo5face/cfgs/yolov5s_face.yaml --hyp models/yolo5face/hyp.scratch.yaml --img 800 --epoch 250 --device 0
# yolov5s_v7.0
$ python3 widerface_train.py --data widerface.yaml --weights "" --cfg models/yolo5face/cfgs/yolov5s_v7_0.yaml --hyp models/yolo5face/hyps/hyp.scratch-low.yaml --img 800 --epoch 300 --device 0
# yolov5s-face
$ python3 widerface_train.py --data widerface.yaml --weights "" --cfg models/yolo5face/cfgs/yolov5s_face.yaml --hyp models/yolo5face/hyps/hyp.scratch.yaml --img 800 --epoch 300 --device 0
```

### 评估

```shell
# python widerface_detect.py --weights ./runs/train/exp4-yolov5s-e250-img800.pt --source ../datasets/widerface/images/val/ --folder_pict ../datasets/widerface/wider_face_split/wider_face_val_bbx_gt.txt --conf-thres 0.001 --iou-thres 0.6 --save-txt --save-conf --device 0
$ python widerface_detect.py --weights ./runs/exp4-yolov5s_v7_0-i800-e300.pt --source ../datasets/widerface/images/val/ --folder_pict ../datasets/widerface/wider_face_split/wider_face_val_bbx_gt.txt --conf-thres 0.001 --iou-thres 0.6 --save-txt --save-conf --device 0
...
YOLOv5s summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs
YOLOv5s_v7_0 summary: 157 layers, 7039792 parameters, 0 gradients, 15.8 GFLOPs
...
Speed: 0.3ms pre-process, 9.0ms inference, 0.9ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp5
0 labels saved to runs/detect/exp5/labels
# cd widerface_evaluate/
# python3 evaluation.py -p ../runs/detect/exp5/labels/ -g ./ground_truth/
Reading Predictions : 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:00<00:00, 94.45it/s]
Processing easy: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:19<00:00, 3.13it/s]
Processing medium: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:19<00:00, 3.12it/s]
Processing hard: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:19<00:00, 3.13it/s]
Speed: 0.4ms pre-process, 8.8ms inference, 0.8ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp7
0 labels saved to runs/detect/exp7/labels
$ cd widerface_evaluate/
$ python3 evaluation.py -p ../runs/detect/exp7/labels/ -g ./ground_truth/
Reading Predictions : 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:00<00:00, 62.18it/s]
Processing easy: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:20<00:00, 2.94it/s]
Processing medium: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:20<00:00, 2.98it/s]
Processing hard: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:20<00:00, 2.97it/s]
==================== Results ====================
Easy Val AP: 0.9520941964576021
Medium Val AP: 0.9341770033021547
Hard Val AP: 0.8403303849682994
Easy Val AP: 0.9483604102331251
Medium Val AP: 0.9328484206418773
Hard Val AP: 0.8467345083774318
=================================================
```

### 预测

```shell
python detect.py --weights ./runs/exp4-yolov5s-e250-img800.pt --source assets/selfie.jpg --imgsz 2048 --conf-thres 0.25 --iou-thres 0.45 --max-det 3000 --hide-labels --hide-conf
$ python detect_face_and_landmarks.py --weights ./runs/exp4-yolov5s_v7_0-i800-e300.pt --source assets/selfie.jpg --imgsz 2048 --conf-thres 0.25 --iou-thres 0.45 --hide-labels --hide-conf
```

![](./assets/results/selfie.jpg)
Expand Down

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