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97 lines
3.3 KiB
Markdown
97 lines
3.3 KiB
Markdown
8 months ago
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# CT
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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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> [CentripetalText: An Efficient Text Instance Representation for Scene Text Detection](https://arxiv.org/abs/2107.05945)
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> Tao Sheng, Jie Chen, Zhouhui Lian
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> NeurIPS, 2021
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On the Total-Text 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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|CT|ResNet18_vd|[configs/det/det_r18_vd_ct.yml](../../configs/det/det_r18_vd_ct.yml)|88.68%|81.70%|85.05%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r18_ct_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 CT model is trained using the Total-Text text detection public dataset. For the download of the dataset, please refer to [Total-Text-Dataset](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset). PaddleOCR format annotation download link [train.txt](https://paddleocr.bj.bcebos.com/dataset/ct_tipc/train.txt), [test.txt](https://paddleocr.bj.bcebos.com/dataset/ct_tipc/test.txt).
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Please refer to [text detection training tutorial](./detection_en.md). 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 CT text detection training process into an inference model. Taking the model based on the Resnet18_vd backbone network and trained on the Total Text English dataset as example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r18_ct_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_r18_vd_ct.yml -o Global.pretrained_model=./det_r18_ct_train/best_accuracy Global.save_inference_dir=./inference/det_ct
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```
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CT text detection model inference, 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/img623.jpg" --det_model_dir="./inference/det_ct/" --det_algorithm="CT"
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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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<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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## Citation
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```bibtex
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@inproceedings{sheng2021centripetaltext,
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title={CentripetalText: An Efficient Text Instance Representation for Scene Text Detection},
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author={Tao Sheng and Jie Chen and Zhouhui Lian},
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booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
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year={2021}
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}
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```
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