doc: add doc for satrn (#11397)
Weihang Wang
GitHub
parent
0382bfb02d
commit
25ffa816f7
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@ -88,6 +88,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
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- [x] [RFL](./algorithm_rec_rfl.md)
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- [x] [ParseQ](./algorithm_rec_parseq.md)
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- [x] [CPPD](./algorithm_rec_cppd.md)
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- [x] [SATRN](./algorithm_rec_satrn.md)
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参考[DTRB](https://arxiv.org/abs/1904.01906)[3]文字识别训练和评估流程，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，算法效果如下：
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@ -114,7 +115,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
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|RFL|ResNetRFL| 88.63% | rec_resnet_rfl_att | [训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl_att_train.tar) |
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|ParseQ|VIT| 91.24% | rec_vit_parseq_synth | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/parseq/rec_vit_parseq_synth.tgz) |
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|CPPD|SVTR-Base| 93.8% | rec_svtrnet_cppd_base_en | [训练模型](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_en_train.tar) |
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|SATRN|ShallowCNN| 88.05% | rec_satrn | [训练模型](https://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a) |
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<a name="13"></a>
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@ -0,0 +1,112 @@
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# SATRN
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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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> [On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention](https://arxiv.org/abs/1910.04396)
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> Junyeop Lee, Sungrae Park, Jeonghun Baek, Seong Joon Oh, Seonghyeon Kim, Hwalsuk Lee
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> CVPR, 2020
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参考[DTRB](https://arxiv.org/abs/1904.01906) 文字识别训练和评估流程，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，算法效果如下：
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|模型|骨干网络|Avg Accuracy|配置文件|下载链接|
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|---|---|---|---|---|
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|SATRN|ShallowCNN|88.05%|[configs/rec/rec_satrn.yml](../../configs/rec/rec_satrn.yml)|[训练模型](https://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a)|
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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[文本识别训练教程](./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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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_satrn.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 rec_satrn.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_satrn.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_satrn.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SATRN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://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a) )，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_satrn.yml -o Global.pretrained_model=./rec_satrn/best_accuracy Global.save_inference_dir=./inference/rec_satrn
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```
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SATRN文本识别模型推理，可以执行如下命令：
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```
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_satrn/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="SATRN" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False
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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SATRN，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{lee2019recognizing,
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title={On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention},
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author={Junyeop Lee and Sungrae Park and Jeonghun Baek and Seong Joon Oh and Seonghyeon Kim and Hwalsuk Lee},
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year={2019},
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eprint={1910.04396},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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@ -85,6 +85,7 @@ Supported text recognition algorithms (Click the link to get the tutorial):
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- [x] [RFL](./algorithm_rec_rfl_en.md)
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- [x] [ParseQ](./algorithm_rec_parseq.md)
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- [x] [CPPD](./algorithm_rec_cppd_en.md)
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- [x] [SATRN](./algorithm_rec_satrn_en.md)
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Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
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@ -111,6 +112,7 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
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|RFL|ResNetRFL| 88.63% | rec_resnet_rfl_att | [trained model](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl_att_train.tar) |
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|ParseQ|VIT| 91.24% | rec_vit_parseq_synth | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/parseq/rec_vit_parseq_synth.tgz) |
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|CPPD|SVTR-Base| 93.8% | rec_svtrnet_cppd_base_en | [trained model](https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_en_train.tar) |
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|SATRN|ShallowCNN| 88.05% | rec_satrn | [trained model](https://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a) |
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<a name="13"></a>
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@ -0,0 +1,111 @@
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# SATRN
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- [1. Introduction](#1)
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- [2. Environment](#2)
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- [3. Model Training / Evaluation / Prediction](#3)
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- [3.1 Training](#3-1)
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- [3.2 Evaluation](#3-2)
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- [3.3 Prediction](#3-3)
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- [4. Inference and Deployment](#4)
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- [4.1 Python Inference](#4-1)
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- [4.2 C++ Inference](#4-2)
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- [4.3 Serving](#4-3)
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- [4.4 More](#4-4)
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- [5. FAQ](#5)
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<a name="1"></a>
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## 1. Introduction
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论文信息：
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> [On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention](https://arxiv.org/abs/1910.04396)
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> Junyeop Lee, Sungrae Park, Jeonghun Baek, Seong Joon Oh, Seonghyeon Kim, Hwalsuk Lee
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> CVPR, 2020
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Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
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|Model|Backbone|config|Acc|Download link|
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| --- | --- | --- | --- | --- |
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|SATRN|ShallowCNN|88.05%|[configs/rec/rec_satrn.yml](../../configs/rec/rec_satrn.yml)|[训练模型](https://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a)|
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<a name="2"></a>
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## 2. Environment
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Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
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<a name="3"></a>
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## 3. Model Training / Evaluation / Prediction
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Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
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Training:
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Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
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```
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#Single GPU training (long training period, not recommended)
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python3 tools/train.py -c configs/rec/rec_satrn.yml
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#Multi GPU training, specify the gpu number through the --gpus parameter
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python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_satrn.yml
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```
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Evaluation:
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```
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# GPU evaluation
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python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
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```
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Prediction:
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```
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# The configuration file used for prediction must match the training
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python3 tools/infer_rec.py -c configs/rec/rec_satrn.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. Inference and Deployment
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<a name="4-1"></a>
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### 4.1 Python Inference
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First, the model saved during the SATRN text recognition training process is converted into an inference model. ( [Model download link](https://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a) ), you can use the following command to convert:
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```
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python3 tools/export_model.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model=./rec_satrn_train/best_accuracy Global.save_inference_dir=./inference/rec_satrn
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```
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For SATRN text recognition model inference, the following commands can be executed:
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```
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_satrn/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="SATRN" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False
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```
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<a name="4-2"></a>
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### 4.2 C++ Inference
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Not supported
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<a name="4-3"></a>
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### 4.3 Serving
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Not supported
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<a name="4-4"></a>
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### 4.4 More
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Not supported
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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{lee2019recognizing,
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title={On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention},
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author={Junyeop Lee and Sungrae Park and Jeonghun Baek and Seong Joon Oh and Seonghyeon Kim and Hwalsuk Lee},
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year={2019},
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eprint={1910.04396},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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