2022-04-28 10:28:14 +08:00
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# SRN
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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. 模型训练、评估、预测](#3)
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- [3.1 训练](#3-1)
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- [3.2 评估](#3-2)
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- [3.3 预测](#3-3)
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- [4. 推理部署](#4)
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- [4.1 Python推理](#4-1)
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- [4.2 C++推理](#4-2)
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- [4.3 Serving服务化部署](#4-3)
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- [4.4 更多推理部署](#4-4)
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- [5. FAQ](#5)
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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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> [Towards Accurate Scene Text Recognition with Semantic Reasoning Networks](https://arxiv.org/abs/2003.12294#)
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> Deli Yu, Xuan Li, Chengquan Zhang, Junyu Han, Jingtuo Liu, Errui Ding
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> CVPR,2020
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使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法复现效果如下：
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|模型|骨干网络|配置文件|Acc|下载链接|
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2022-04-28 19:23:53 +08:00
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| --- | --- | --- | --- | --- |
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2022-04-28 10:28:14 +08:00
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|SRN|Resnet50_vd_fpn|[rec_r50_fpn_srn.yml](../../configs/rec/rec_r50_fpn_srn.yml)|86.31%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_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)克隆项目代码。
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<a name="3"></a>
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## 3. 模型训练、评估、预测
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请参考[文本识别教程](./recognition.md)。PaddleOCR对代码进行了模块化，训练不同的识别模型只需要**更换配置文件**即可。
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训练
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具体地，在完成数据准备后，便可以启动训练，训练命令如下：
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```
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#单卡训练（训练周期长，不建议）
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python3 tools/train.py -c configs/rec/rec_r50_fpn_srn.yml
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#多卡训练，通过--gpus参数指定卡号
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python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r50_fpn_srn.yml
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```
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评估
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```
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# GPU 评估， Global.pretrained_model 为待测权重
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python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r50_fpn_srn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
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```
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预测：
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```
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# 预测使用的配置文件必须与训练一致
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python3 tools/infer_rec.py -c configs/rec/rec_r50_fpn_srn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
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```
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<a name="4"></a>
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## 4. 推理部署
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<a name="4-1"></a>
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### 4.1 Python推理
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首先将SRN文本识别训练过程中保存的模型，转换成inference model。（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_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/rec/rec_r50_fpn_srn.yml -o Global.pretrained_model=./rec_r50_vd_srn_train/best_accuracy Global.save_inference_dir=./inference/rec_srn
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```
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SRN文本识别模型推理，可以执行如下命令：
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```
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2022-08-22 16:03:12 +08:00
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_srn/" --rec_image_shape="1,64,256" --rec_algorithm="SRN" --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --use_space_char=False
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2022-04-28 10:28:14 +08:00
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```
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<a name="4-2"></a>
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### 4.2 C++推理
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由于C++预处理后处理还未支持SRN，所以暂未支持
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<a name="4-3"></a>
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### 4.3 Serving服务化部署
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暂不支持
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<a name="4-4"></a>
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### 4.4 更多推理部署
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暂不支持
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<a name="5"></a>
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## 5. FAQ
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## 引用
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```bibtex
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@article{Yu2020TowardsAS,
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title={Towards Accurate Scene Text Recognition With Semantic Reasoning Networks},
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author={Deli Yu and Xuan Li and Chengquan Zhang and Junyu Han and Jingtuo Liu and Errui Ding},
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journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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year={2020},
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pages={12110-12119}
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}
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```
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