108 lines
4.3 KiB
Markdown
108 lines
4.3 KiB
Markdown
8 months ago
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# PSENet
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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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Paper:
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> [Shape robust text detection with progressive scale expansion network](https://arxiv.org/abs/1903.12473)
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> Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai
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> CVPR, 2019
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On the ICDAR2015 dataset, the text detection result is as follows:
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|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
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| --- | --- | --- | --- | --- | --- | --- |
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|PSE| ResNet50_vd | [configs/det/det_r50_vd_pse.yml](../../configs/det/det_r50_vd_pse.yml)| 85.81% |79.53%|82.55%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
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|PSE| MobileNetV3| [configs/det/det_mv3_pse.yml](../../configs/det/det_mv3_pse.yml) | 82.20% |70.48%|75.89%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
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<a name="2"></a>
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## 2. Environment
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Please prepare your environment referring to [prepare the environment](./environment_en.md) and [clone the repo](./clone_en.md).
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<a name="3"></a>
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## 3. Model Training / Evaluation / Prediction
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The above PSE model is trained using the ICDAR2015 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md).
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After the data download is complete, please refer to [Text Detection Training Tutorial](./detection_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models.
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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, convert the model saved in the PSE text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)), you can use the following command to convert:
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```shell
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python3 tools/export_model.py -c configs/det/det_r50_vd_pse.yml -o Global.pretrained_model=./det_r50_vd_pse_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_pse
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```
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PSE text detection model inference, to perform non-curved text detection, you can run the following commands:
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```shell
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python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_pse/" --det_algorithm="PSE" --det_pse_box_type=quad
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```
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The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
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If you want to perform curved text detection, you can execute the following command:
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```shell
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python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_pse/" --det_algorithm="PSE" --det_pse_box_type=poly
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```
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The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
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**Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese or curved text images.
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<a name="4-2"></a>
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### 4.2 C++ Inference
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Since the post-processing is not written in CPP, the PSE text detection model does not support CPP inference.
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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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## Citation
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```bibtex
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@inproceedings{wang2019shape,
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title={Shape robust text detection with progressive scale expansion network},
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author={Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai},
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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pages={9336--9345},
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year={2019}
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}
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```
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