提交 28e032c7 编写于 作者: A andyjpaddle

add visionlan doc

上级 0d3c2924
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- [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) |
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......
# 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)
<a name="1"></a>
## 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
<a name="model"></a>
`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)|
<a name="2"></a>
## 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。
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## 3. 模型训练、评估、预测
<a name="3-1"></a>
### 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**`VisionLAN`[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
```
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### 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
```
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### 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/'。
```
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## 4. 推理部署
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### 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。
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### 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
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### 4.3 Serving服务化部署
暂不支持
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### 4.4 更多推理部署
暂不支持
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## 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}
}
```
......@@ -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) |
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......
# 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)
<a name="1"></a>
## 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)|
<a name="2"></a>
## 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.
<a name="3"></a>
## 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
```
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## 4. Inference and Deployment
<a name="4-1"></a>
### 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)
```
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### 4.2 C++ Inference
Not supported
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### 4.3 Serving
Not supported
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### 4.4 More
Not supported
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## 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}
}
```
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