diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index 5c7adc715f1a5e728d9320c62dc15c578d9f18bf..02acd2297bbc4b2805456d1457473c6ef168d452 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -69,6 +69,7 @@ - [x] [SVTR](./algorithm_rec_svtr.md) - [x] [ViTSTR](./algorithm_rec_vitstr.md) - [x] [ABINet](./algorithm_rec_abinet.md) +- [x] [VisionLAN](./algorithm_rec_visionlan.md) 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： @@ -89,6 +90,7 @@ |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) | |ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) | |ABINet|Resnet45| 90.75% | rec_r45_abinet | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) | +|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar) | diff --git a/doc/doc_ch/algorithm_rec_visionlan.md b/doc/doc_ch/algorithm_rec_visionlan.md new file mode 100644 index 0000000000000000000000000000000000000000..0c4fe86e58831f4f5480483f5c21ff1da4176d2b --- /dev/null +++ b/doc/doc_ch/algorithm_rec_visionlan.md @@ -0,0 +1,154 @@ +# 场景文本识别算法-VisionLAN + +- [1. 算法简介](#1) +- [2. 环境配置](#2) +- [3. 模型训练、评估、预测](#3) + - [3.1 训练](#3-1) + - [3.2 评估](#3-2) + - [3.3 预测](#3-3) +- [4. 推理部署](#4) + - [4.1 Python推理](#4-1) + - [4.2 C++推理](#4-2) + - [4.3 Serving服务化部署](#4-3) + - [4.4 更多推理部署](#4-4) +- [5. FAQ](#5) + + +## 1. 算法简介 + +论文信息： +> [From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network](https://arxiv.org/abs/2108.09661) +> Yuxin Wang, Hongtao Xie, Shancheng Fang, Jing Wang, Shenggao Zhu, Yongdong Zhang +> ICCV, 2021 + + + +`VisionLAN`使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC13, IC15, SVTP, CUTE数据集上进行评估，算法复现效果如下： + +|模型|骨干网络|配置文件|Acc|下载链接| +| --- | --- | --- | --- | --- | +|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.3%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar)| + + +## 2. 环境配置 +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。 + + + +## 3. 模型训练、评估、预测 + + +### 3.1 模型训练 + +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`VisionLAN`识别模型时需要**更换配置文件**为`VisionLAN`的[配置文件](../../configs/rec/rec_r45_visionlan.yml)。 + +#### 启动训练 + + +具体地，在完成数据准备后，便可以启动训练，训练命令如下： +```shell +#单卡训练（训练周期长，不建议） +python3 tools/train.py -c configs/rec/rec_r45_visionlan.yml + +#多卡训练，通过--gpus参数指定卡号 +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r45_visionlan.yml +``` + + +### 3.2 评估 + +可下载已训练完成的[模型文件](#model)，使用如下命令进行评估： + +```shell +# 注意将pretrained_model的路径设置为本地路径。 +python3 tools/eval.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy +``` + + +### 3.3 预测 + +使用如下命令进行单张图片预测： +```shell +# 注意将pretrained_model的路径设置为本地路径。 +python3 tools/infer_rec.py -c configs/rec/rec_r45_visionlan.yml -o Global.infer_img='./doc/imgs_words/en/word_2.png' Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy +# 预测文件夹下所有图像时，可修改infer_img为文件夹，如 Global.infer_img='./doc/imgs_words_en/'。 +``` + + + +## 4. 推理部署 + + +### 4.1 Python推理 +首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar))，可以使用如下命令进行转换： + +```shell +# 注意将pretrained_model的路径设置为本地路径。 +python3 tools/export_model.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy Global.save_inference_dir=./inference/rec_r45_visionlan/ +``` +**注意：** +- 如果您是在自己的数据集上训练的模型，并且调整了字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。 +- 如果您修改了训练时的输入大小，请修改`tools/export_model.py`文件中的对应VisionLAN的`infer_shape`。 + +转换成功后，在目录下有三个文件： +``` +./inference/rec_r45_visionlan/ + ├── inference.pdiparams # 识别inference模型的参数文件 + ├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略 + └── inference.pdmodel # 识别inference模型的program文件 +``` + +执行如下命令进行模型推理： + +```shell +python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/dict36.txt' +# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/imgs_words_en/'。 +``` + +![](../imgs_words/en/word_2.png) + +执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下： +结果如下： +```shell +Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.97076982) +``` + +**注意**： + +- 训练上述模型采用的图像分辨率是[3，64，256]，需要通过参数`rec_image_shape`设置为您训练时的识别图像形状。 +- 在推理时需要设置参数`rec_char_dict_path`指定字典，如果您修改了字典，请修改该参数为您的字典文件。 +- 如果您修改了预处理方法，需修改`tools/infer/predict_rec.py`中VisionLAN的预处理为您的预处理方法。 + + + +### 4.2 C++推理部署 + +由于C++预处理后处理还未支持VisionLAN，所以暂未支持 + + +### 4.3 Serving服务化部署 + +暂不支持 + + +### 4.4 更多推理部署 + +暂不支持 + + +## 5. FAQ + +1. MJSynth和SynthText两种数据集来自于[VisionLAN源repo](https://github.com/wangyuxin87/VisionLAN) 。 +2. 我们使用VisionLAN作者提供的预训练模型进行finetune训练。 + +## 引用 + +```bibtex +@inproceedings{wang2021two, + title={From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network}, + author={Wang, Yuxin and Xie, Hongtao and Fang, Shancheng and Wang, Jing and Zhu, Shenggao and Zhang, Yongdong}, + booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, + pages={14194--14203}, + year={2021} +} +``` diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index f3c96b620c94c3b5f795b6117a7c6bcfcfa43b7a..1b82d1ffc32fe5a473382c74ee90f346ba875e1b 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -68,6 +68,7 @@ Supported text recognition algorithms (Click the link to get the tutorial): - [x] [SVTR](./algorithm_rec_svtr_en.md) - [x] [ViTSTR](./algorithm_rec_vitstr_en.md) - [x] [ABINet](./algorithm_rec_abinet_en.md) +- [x] [VisionLAN](./algorithm_rec_visionlan_en.md) 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: @@ -88,7 +89,7 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) | |ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) | |ABINet|Resnet45| 90.75% | rec_r45_abinet | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) | - +|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar) | diff --git a/doc/doc_en/algorithm_rec_visionlan_en.md b/doc/doc_en/algorithm_rec_visionlan_en.md new file mode 100644 index 0000000000000000000000000000000000000000..ebd02d52f4252c672b4a76c940ccdd621f5354ef --- /dev/null +++ b/doc/doc_en/algorithm_rec_visionlan_en.md @@ -0,0 +1,135 @@ +# VisionLAN + +- [1. Introduction](#1) +- [2. Environment](#2) +- [3. Model Training / Evaluation / Prediction](#3) + - [3.1 Training](#3-1) + - [3.2 Evaluation](#3-2) + - [3.3 Prediction](#3-3) +- [4. Inference and Deployment](#4) + - [4.1 Python Inference](#4-1) + - [4.2 C++ Inference](#4-2) + - [4.3 Serving](#4-3) + - [4.4 More](#4-4) +- [5. FAQ](#5) + + +## 1. Introduction + +Paper: +> [From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network](https://arxiv.org/abs/2108.09661) +> Yuxin Wang, Hongtao Xie, Shancheng Fang, Jing Wang, Shenggao Zhu, Yongdong Zhang +> ICCV, 2021 + +Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows: + +|Model|Backbone|config|Acc|Download link| +| --- | --- | --- | --- | --- | +|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.3%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar)| + + +## 2. Environment +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. + + + +## 3. Model Training / Evaluation / Prediction + +Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. + +Training: + +Specifically, after the data preparation is completed, the training can be started. The training command is as follows: + +``` +#Single GPU training (long training period, not recommended) +python3 tools/train.py -c configs/rec/rec_r45_visionlan.yml + +#Multi GPU training, specify the gpu number through the --gpus parameter +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r45_visionlan.yml +``` + +Evaluation: + +``` +# GPU evaluation +python3 tools/eval.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model={path/to/weights}/best_accuracy +``` + +Prediction: + +``` +# The configuration file used for prediction must match the training +python3 tools/infer_rec.py -c configs/rec/rec_r45_visionlan.yml -o Global.infer_img='./doc/imgs_words/en/word_2.png' Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy +``` + + +## 4. Inference and Deployment + + +### 4.1 Python Inference +First, the model saved during the VisionLAN text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar)) ), you can use the following command to convert: + +``` +python3 tools/export_model.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy Global.save_inference_dir=./inference/rec_r45_visionlan/ +``` + +**Note:** +- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file. +- If you modified the input size during training, please modify the `infer_shape` corresponding to VisionLAN in the `tools/export_model.py` file. + +After the conversion is successful, there are three files in the directory: +``` +./inference/rec_r45_visionlan/ + ├── inference.pdiparams + ├── inference.pdiparams.info + └── inference.pdmodel +``` + + +For VisionLAN text recognition model inference, the following commands can be executed: + +``` +python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/dict36.txt' +``` + +![](../imgs_words/en/word_2.png) + +After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows: +The result is as follows: +```shell +Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.97076982) +``` + + +### 4.2 C++ Inference + +Not supported + + +### 4.3 Serving + +Not supported + + +### 4.4 More + +Not supported + + +## 5. FAQ + +1. Note that the MJSynth and SynthText datasets come from [VisionLAN repo](https://github.com/wangyuxin87/VisionLAN). +2. We use the pre-trained model provided by the VisionLAN authors for finetune training. + +## Citation + +```bibtex +@inproceedings{wang2021two, + title={From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network}, + author={Wang, Yuxin and Xie, Hongtao and Fang, Shancheng and Wang, Jing and Zhu, Shenggao and Zhang, Yongdong}, + booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, + pages={14194--14203}, + year={2021} +} +```