更换文档检测模型
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paddle_detection/configs/face_detection/README_en.md
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paddle_detection/configs/face_detection/README_en.md
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# Face Detection Model
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## Introduction
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`face_detection` High efficiency, high speed face detection solutions, including the most advanced models and classic models.
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## Model Library
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#### A mAP on the WIDERFACE dataset
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| Network structure | size | images/GPUs | Learning rate strategy | Easy/Medium/Hard Set | Prediction delay（SD855）| Model size(MB) | Download | Configuration File |
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|:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|
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| BlazeFace | 640 | 8 | 1000e | 0.885 / 0.855 / 0.731 | - | 0.472 |[link](https://paddledet.bj.bcebos.com/models/blazeface_1000e.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/face_detection/blazeface_1000e.yml) |
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| BlazeFace-FPN-SSH | 640 | 8 | 1000e | 0.907 / 0.883 / 0.793 | - | 0.479 |[link](https://paddledet.bj.bcebos.com/models/blazeface_fpn_ssh_1000e.pdparams) | [Configuration File](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/face_detection/blazeface_fpn_ssh_1000e.yml) |
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**Attention:**
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- We use a multi-scale evaluation strategy to get the mAP in `Easy/Medium/Hard Set`. Please refer to the [evaluation on the WIDER FACE dataset](#Evaluated-on-the-WIDER-FACE-Dataset) for details.
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## Quick Start
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### Data preparation
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We use [WIDER-FACE dataset](http://shuoyang1213.me/WIDERFACE/) for training and model tests, the official web site provides detailed data is introduced.
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- WIDER-Face data source:
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- Load a dataset of type `wider_face` using the following directory structure:
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```
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dataset/wider_face/
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├── wider_face_split
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│ ├── wider_face_train_bbx_gt.txt
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│ ├── wider_face_val_bbx_gt.txt
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├── WIDER_train
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│ ├── images
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│ │ ├── 0--Parade
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│ │ │ ├── 0_Parade_marchingband_1_100.jpg
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│ │ │ ├── 0_Parade_marchingband_1_381.jpg
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│ │ │ │ ...
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│ │ ├── 10--People_Marching
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│ │ │ ...
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├── WIDER_val
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│ ├── images
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│ │ ├── 0--Parade
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│ │ │ ├── 0_Parade_marchingband_1_1004.jpg
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│ │ │ ├── 0_Parade_marchingband_1_1045.jpg
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│ │ │ │ ...
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│ │ ├── 10--People_Marching
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│ │ │ ...
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```
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- Manually download the dataset:
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To download the WIDER-FACE dataset, run the following command:
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```
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cd dataset/wider_face && ./download_wider_face.sh
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```
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### Parameter configuration
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The configuration of the base model can be referenced to `configs/face_detection/_base_/blazeface.yml`；
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Improved model to add FPN and SSH neck structure, configuration files can be referenced to `configs/face_detection/_base_/blazeface_fpn.yml`, You can configure FPN and SSH as required
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```yaml
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BlazeNet:
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blaze_filters: [[24, 24], [24, 24], [24, 48, 2], [48, 48], [48, 48]]
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double_blaze_filters: [[48, 24, 96, 2], [96, 24, 96], [96, 24, 96],
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[96, 24, 96, 2], [96, 24, 96], [96, 24, 96]]
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act: hard_swish #Configure Blaze Block activation function in Backbone. The basic model is Relu. hard_swish is needed to add FPN and SSH
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BlazeNeck:
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neck_type : fpn_ssh #only_fpn, only_ssh and fpn_ssh
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in_channel: [96,96]
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```
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### Training and Evaluation
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The training process and evaluation process methods are consistent with other algorithms, please refer to [GETTING_STARTED_cn.md](../../docs/tutorials/GETTING_STARTED_cn.md)。
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**Attention:** Face detection models currently do not support training and evaluation.
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#### Evaluated on the WIDER-FACE Dataset
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- Step 1: Evaluate and generate a result file:
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```shell
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python -u tools/eval.py -c configs/face_detection/blazeface_1000e.yml \
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-o weights=output/blazeface_1000e/model_final \
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multi_scale=True
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```
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Set `multi_scale=True` for multi-scale evaluation. After evaluation, test results in TXT format will be generated in `output/pred`.
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- Step 2: Download the official evaluation script and Ground Truth file:
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```
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wget http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/eval_script/eval_tools.zip
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unzip eval_tools.zip && rm -f eval_tools.zip
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```
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- Step 3: Start the evaluation
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Method 1: Python evaluation:
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```
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git clone https://github.com/wondervictor/WiderFace-Evaluation.git
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cd WiderFace-Evaluation
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# compile
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python3 setup.py build_ext --inplace
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# Begin to assess
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python3 evaluation.py -p /path/to/PaddleDetection/output/pred -g /path/to/eval_tools/ground_truth
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```
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Method 2: MatLab evaluation:
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```
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# Change the name of save result path and draw curve in `eval_tools/wider_eval.m`:
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pred_dir = './pred';
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legend_name = 'Paddle-BlazeFace';
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`wider_eval.m` is the main implementation of the evaluation module. Run the following command:
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matlab -nodesktop -nosplash -nojvm -r "run wider_eval.m;quit;"
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```
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### Use by Python Code
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In order to support development, here is an example of using the Paddle Detection whl package to make predictions through Python code.
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```python
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import cv2
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import paddle
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import numpy as np
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from ppdet.core.workspace import load_config
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from ppdet.engine import Trainer
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from ppdet.metrics import get_infer_results
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from ppdet.data.transform.operators import NormalizeImage, Permute
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if __name__ == '__main__':
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# prepare for the parameters
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config_path = 'PaddleDetection/configs/face_detection/blazeface_1000e.yml'
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cfg = load_config(config_path)
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weight_path = 'PaddleDetection/output/blazeface_1000e.pdparams'
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infer_img_path = 'PaddleDetection/demo/hrnet_demo.jpg'
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cfg.weights = weight_path
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bbox_thre = 0.8
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paddle.set_device('gpu')
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# create the class object
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trainer = Trainer(cfg, mode='test')
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trainer.load_weights(cfg.weights)
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trainer.model.eval()
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normaler = NormalizeImage(mean=[123, 117, 104], std=[127.502231, 127.502231, 127.502231], is_scale=False)
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permuter = Permute()
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# read the image file
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im = cv2.imread(infer_img_path)
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im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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# prepare for the data dict
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data_dict = {'image': im}
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data_dict = normaler(data_dict)
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data_dict = permuter(data_dict)
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h, w, c = im.shape
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data_dict['im_id'] = paddle.Tensor(np.array([[0]]))
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data_dict['im_shape'] = paddle.Tensor(np.array([[h, w]], dtype=np.float32))
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data_dict['scale_factor'] = paddle.Tensor(np.array([[1., 1.]], dtype=np.float32))
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data_dict['image'] = paddle.Tensor(data_dict['image'].reshape((1, c, h, w)))
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data_dict['curr_iter'] = paddle.Tensor(np.array([0]))
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# do the prediction
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outs = trainer.model(data_dict)
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# to do the postprocess to get the final bbox info
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for key in ['im_shape', 'scale_factor', 'im_id']:
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outs[key] = data_dict[key]
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for key, value in outs.items():
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outs[key] = value.numpy()
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clsid2catid, catid2name = {0: 'face'}, {0: 0}
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batch_res = get_infer_results(outs, clsid2catid)
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bbox = [sub_dict for sub_dict in batch_res['bbox'] if sub_dict['score'] > bbox_thre]
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print(bbox)
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```
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## Citations
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```
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@article{bazarevsky2019blazeface,
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title={BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs},
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author={Valentin Bazarevsky and Yury Kartynnik and Andrey Vakunov and Karthik Raveendran and Matthias Grundmann},
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year={2019},
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eprint={1907.05047},
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archivePrefix={arXiv},
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```
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