119 lines
4.3 KiB
Markdown
119 lines
4.3 KiB
Markdown
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# DB
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- [1. 算法简介](#1)
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- [2. 环境配置](#2)
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- [3. 快速使用](#3)
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- [4. 模型训练、评估、预测](#4)
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- [5. 推理部署](#5)
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- [6. FAQ](#6)
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<a name="1"></a>
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## 1. 算法简介
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论文信息：
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> [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/abs/1911.08947)
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> Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang
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> AAAI, 2020
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在ICDAR2015文本检测公开数据集上，算法复现效果如下：
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|模型|骨干网络|precision|recall|Hmean|下载链接|
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| --- | --- | --- | --- | --- | --- |
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|DB|ResNet50_vd|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
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|DB|MobileNetV3|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
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<a name="2"></a>
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## 2. 环境配置
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请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目
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<a name="3"></a>
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## 3. 快速使用
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参考本节，可以直接下载训好的模型，进行基于训练引擎的模型预测。
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### 训练模型下载
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根据第1节给出的模型列表，选择下载训练模型：
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```bash
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mkdir trained_models && cd trained_models
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar && tar xf det_mv3_db_v2.0_train.tar
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cd ..
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```
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* windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下
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解压完毕后应有如下文件结构：
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```
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├── det_mv3_db_v2.0_train
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│ ├── best_accuracy.states
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│ ├── best_accuracy.pdparams
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│ ├── best_accuracy.pdopt
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│ └── train.log
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```
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### 单张图像或者图像集合预测
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```bash
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# 预测image_dir指定的单张图像
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python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_valid_set="totaltext"
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# 预测image_dir指定的图像集合
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python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_valid_set="totaltext"
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# 如果想使用CPU进行预测，需设置use_gpu参数为False
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python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_valid_set="totaltext" --use_gpu=False
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```
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### 可视化结果
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可视化文本检测结果默认保存到./inference_results文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
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<a name="4"></a>
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## 4. 模型训练、评估、预测
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### 4.1 训练
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### 4.2 评估
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### 4.3 预测
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<a name="5"></a>
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## 5. 推理部署
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### 5.1 Python推理
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首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar) )，可以使用如下命令进行转换：
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```
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python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_db
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```
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DB文本检测模型推理，可以执行如下命令：
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```
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python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
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```
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可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
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**注意**：由于ICDAR2015数据集只有1000张训练图像，且主要针对英文场景，所以上述模型对中文文本图像检测效果会比较差。
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### 5.2 C++推理
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敬请期待
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### 5.3 Serving服务化部署
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敬请期待
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### 5.4 Paddle2ONNX推理
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敬请期待
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<a name="6"></a>
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## 6. FAQ
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## 引用
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```bibtex
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@inproceedings{liao2020real,
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title={Real-time scene text detection with differentiable binarization},
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author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
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booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
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volume={34},
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number={07},
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pages={11474--11481},
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year={2020}
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}
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```
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