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add blazeface & update rcnn modelzoo (PaddlePaddle#2303)
* add blazeface & update rcnn modelzoo * remove condition num_priors
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| metric: WiderFace | ||
| num_classes: 1 | ||
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| TrainDataset: | ||
| !WIDERFaceDataSet | ||
| dataset_dir: dataset/wider_face | ||
| anno_path: wider_face_split/wider_face_train_bbx_gt.txt | ||
| image_dir: WIDER_train/images | ||
| data_fields: ['image', 'gt_bbox', 'gt_class'] | ||
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| EvalDataset: | ||
| !WIDERFaceDataSet | ||
| dataset_dir: dataset/wider_face | ||
| anno_path: wider_face_split/wider_face_val_bbx_gt.txt | ||
| image_dir: WIDER_val/images | ||
| data_fields: ['image'] | ||
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| TestDataset: | ||
| !ImageFolder | ||
| use_default_label: true |
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| # 人脸检测模型 | ||
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| ## 简介 | ||
| `face_detection`中提供高效、高速的人脸检测解决方案，包括最先进的模型和经典模型。 | ||
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|  | ||
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| ## 模型库 | ||
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| #### WIDER-FACE数据集上的mAP | ||
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| | 网络结构 | 输入尺寸 | 图片个数/GPU | 学习率策略 | Easy/Medium/Hard Set | 预测时延（SD855）| 模型大小(MB) | 下载 | 配置文件 | | ||
| |:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:| | ||
| | BlazeFace | 640 | 8 | 1000e | 0.889 / 0.859 / 0.740 | - | 0.472 |[下载链接](https://paddledet.bj.bcebos.com/models/blazeface_1000e.pdparams) | [配置文件](/~https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/face_detection/blazeface_1000e.yml) | | ||
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| **注意:** | ||
| - 我们使用多尺度评估策略得到`Easy/Medium/Hard Set`里的mAP。具体细节请参考[在WIDER-FACE数据集上评估](#在WIDER-FACE数据集上评估)。 | ||
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| ## 快速开始 | ||
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| ### 数据准备 | ||
| 我们使用[WIDER-FACE数据集](http://shuoyang1213.me/WIDERFACE/)进行训练和模型测试，官方网站提供了详细的数据介绍。 | ||
| - WIDER-Face数据源: | ||
| 使用如下目录结构加载`wider_face`类型的数据集： | ||
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| ``` | ||
| dataset/wider_face/ | ||
| ├── wider_face_split | ||
| │ ├── wider_face_train_bbx_gt.txt | ||
| │ ├── wider_face_val_bbx_gt.txt | ||
| ├── WIDER_train | ||
| │ ├── images | ||
| │ │ ├── 0--Parade | ||
| │ │ │ ├── 0_Parade_marchingband_1_100.jpg | ||
| │ │ │ ├── 0_Parade_marchingband_1_381.jpg | ||
| │ │ │ │ ... | ||
| │ │ ├── 10--People_Marching | ||
| │ │ │ ... | ||
| ├── WIDER_val | ||
| │ ├── images | ||
| │ │ ├── 0--Parade | ||
| │ │ │ ├── 0_Parade_marchingband_1_1004.jpg | ||
| │ │ │ ├── 0_Parade_marchingband_1_1045.jpg | ||
| │ │ │ │ ... | ||
| │ │ ├── 10--People_Marching | ||
| │ │ │ ... | ||
| ``` | ||
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| - 手动下载数据集： | ||
| 要下载WIDER-FACE数据集，请运行以下命令： | ||
| ``` | ||
| cd dataset/wider_face && ./download_wider_face.sh | ||
| ``` | ||
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| ### 训练与评估 | ||
| 训练流程与评估流程方法与其他算法一致，请参考[GETTING_STARTED_cn.md](../../docs/tutorials/GETTING_STARTED_cn.md)。 | ||
| **注意:** | ||
| - 人脸检测模型目前不支持边训练边评估。 | ||
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| #### 在WIDER-FACE数据集上评估 | ||
| 评估并生成结果文件： | ||
| ```shell | ||
| python -u tools/eval.py -c configs/face_detection/blazeface_1000e.yml \ | ||
| -o weights=output/blazeface_1000e/model_final \ | ||
| multi_scale=True | ||
| ``` | ||
| 设置`multi_scale=True`进行多尺度评估，评估完成后，将在`output/pred`中生成txt格式的测试结果。 | ||
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| - 下载官方评估脚本来评估AP指标： | ||
| ``` | ||
| wget http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/eval_script/eval_tools.zip | ||
| unzip eval_tools.zip && rm -f eval_tools.zip | ||
| ``` | ||
| - 在`eval_tools/wider_eval.m`中修改保存结果路径和绘制曲线的名称： | ||
| ``` | ||
| # Modify the folder name where the result is stored. | ||
| pred_dir = './pred'; | ||
| # Modify the name of the curve to be drawn | ||
| legend_name = 'Fluid-BlazeFace'; | ||
| ``` | ||
| - `wider_eval.m` 是评估模块的主要执行程序。运行命令如下： | ||
| ``` | ||
| matlab -nodesktop -nosplash -nojvm -r "run wider_eval.m;quit;" | ||
| ``` | ||
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| ## Citations | ||
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| ``` | ||
| @article{bazarevsky2019blazeface, | ||
| title={BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs}, | ||
| author={Valentin Bazarevsky and Yury Kartynnik and Andrey Vakunov and Karthik Raveendran and Matthias Grundmann}, | ||
| year={2019}, | ||
| eprint={1907.05047}, | ||
| archivePrefix={arXiv}, | ||
| ``` |
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| architecture: SSD | ||
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| SSD: | ||
| backbone: BlazeNet | ||
| ssd_head: FaceHead | ||
| post_process: BBoxPostProcess | ||
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| BlazeNet: | ||
| blaze_filters: [[24, 24], [24, 24], [24, 48, 2], [48, 48], [48, 48]] | ||
| double_blaze_filters: [[48, 24, 96, 2], [96, 24, 96], [96, 24, 96], | ||
| [96, 24, 96, 2], [96, 24, 96], [96, 24, 96]] | ||
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| FaceHead: | ||
| in_channels: [96, 96] | ||
| anchor_generator: AnchorGeneratorSSD | ||
| loss: SSDLoss | ||
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| SSDLoss: | ||
| overlap_threshold: 0.35 | ||
| neg_overlap: 0.35 | ||
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| AnchorGeneratorSSD: | ||
| steps: [8., 16.] | ||
| aspect_ratios: [[1.], [1.]] | ||
| min_sizes: [[16.,24.], [32., 48., 64., 80., 96., 128.]] | ||
| max_sizes: [[], []] | ||
| offset: 0.5 | ||
| flip: False | ||
| min_max_aspect_ratios_order: false | ||
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| BBoxPostProcess: | ||
| decode: | ||
| name: SSDBox | ||
| nms: | ||
| name: MultiClassNMS | ||
| keep_top_k: 750 | ||
| score_threshold: 0.01 | ||
| nms_threshold: 0.3 | ||
| nms_top_k: 5000 | ||
| nms_eta: 1.0 |
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| worker_num: 2 | ||
| TrainReader: | ||
| inputs_def: | ||
| num_max_boxes: 90 | ||
| sample_transforms: | ||
| - Decode: {} | ||
| - RandomDistort: {brightness: [0.5, 1.125, 0.875], random_apply: False} | ||
| - RandomExpand: {fill_value: [123.675, 116.28, 103.53]} | ||
| - RandomFlip: {} | ||
| - CropWithDataAchorSampling: { | ||
| anchor_sampler: [[1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]], | ||
| batch_sampler: [ | ||
| [1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], | ||
| [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], | ||
| [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], | ||
| [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], | ||
| [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], | ||
| ], | ||
| target_size: 640} | ||
| - Resize: {target_size: [640, 640], keep_ratio: False, interp: 1} | ||
| - NormalizeBox: {} | ||
| - PadBox: {num_max_boxes: 90} | ||
| batch_transforms: | ||
| - NormalizeImage: {mean: [123, 117, 104], std: [127.502231, 127.502231, 127.502231], is_scale: false} | ||
| - Permute: {} | ||
| batch_size: 8 | ||
| shuffle: true | ||
| drop_last: true | ||
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| EvalReader: | ||
| sample_transforms: | ||
| - Decode: {} | ||
| - NormalizeImage: {mean: [123, 117, 104], std: [127.502231, 127.502231, 127.502231], is_scale: false} | ||
| - Permute: {} | ||
| batch_size: 1 | ||
| drop_empty: false | ||
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| TestReader: | ||
| sample_transforms: | ||
| - Decode: {} | ||
| - NormalizeImage: {mean: [123, 117, 104], std: [127.502231, 127.502231, 127.502231], is_scale: false} | ||
| - Permute: {} | ||
| batch_size: 1 |
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| epoch: 1000 | ||
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| LearningRate: | ||
| base_lr: 0.001 | ||
| schedulers: | ||
| - !PiecewiseDecay | ||
| gamma: 0.1 | ||
| milestones: | ||
| - 333 | ||
| - 800 | ||
| - !LinearWarmup | ||
| start_factor: 0.3333333333333333 | ||
| steps: 500 | ||
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| OptimizerBuilder: | ||
| optimizer: | ||
| momentum: 0.0 | ||
| type: RMSProp | ||
| regularizer: | ||
| factor: 0.0005 | ||
| type: L2 |
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| _BASE_: [ | ||
| '../datasets/wider_face.yml', | ||
| '../runtime.yml', | ||
| '_base_/optimizer_1000e.yml', | ||
| '_base_/blazeface.yml', | ||
| '_base_/face_reader.yml', | ||
| ] | ||
| weights: output/blazeface_1000e/model_final | ||
| multi_scale_eval: True |
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| # All rights `PaddleDetection` reserved | ||
| # References: | ||
| # @inproceedings{yang2016wider, | ||
| # Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, | ||
| # Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | ||
| # Title = {WIDER FACE: A Face Detection Benchmark}, | ||
| # Year = {2016}} | ||
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| DIR="$( cd "$(dirname "$0")" ; pwd -P )" | ||
| cd "$DIR" | ||
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| # Download the data. | ||
| echo "Downloading..." | ||
| wget https://dataset.bj.bcebos.com/wider_face/WIDER_train.zip | ||
| wget https://dataset.bj.bcebos.com/wider_face/WIDER_val.zip | ||
| wget https://dataset.bj.bcebos.com/wider_face/wider_face_split.zip | ||
| # Extract the data. | ||
| echo "Extracting..." | ||
| unzip -q WIDER_train.zip | ||
| unzip -q WIDER_val.zip | ||
| unzip -q wider_face_split.zip |
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