diff --git a/doc/doc_ch/algorithm_det_fcenet.md b/doc/doc_ch/algorithm_det_fcenet.md new file mode 100644 index 0000000000000000000000000000000000000000..bd2e734204d32bbf575ddea9f889953a72582c59 --- /dev/null +++ b/doc/doc_ch/algorithm_det_fcenet.md @@ -0,0 +1,104 @@ +# FCENet + +- [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： +> [Fourier Contour Embedding for Arbitrary-Shaped Text Detection](https://arxiv.org/abs/2104.10442) +> Yiqin Zhu and Jianyong Chen and Lingyu Liang and Zhanghui Kuang and Lianwen Jin and Wayne Zhang +> CVPR, 2021 + +在CTW1500文本检测公开数据集上，算法复现效果如下： + +| 模型 |骨干网络|配置文件|precision|recall|Hmean|下载链接| +|-----| --- | --- | --- | --- | --- | --- | +| FCE | ResNet50_dcn | [configs/det/det_r50_vd_dcn_fce_ctw.yml](../../configs/det/det_r50_vd_dcn_fce_ctw.yml)| 88.39%|82.18%|85.27%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_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 + +上述FCE模型使用CTW1500文本检测公开数据集训练得到，数据集下载可参考 [ocr_datasets](./dataset/ocr_datasets.md)。 + +数据下载完成后，请参考[文本检测训练教程](./detection.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的检测模型只需要**更换配置文件**即可。 + + + +## 4. 推理部署 + + +### 4.1 Python推理 +首先将FCE文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd_dcn骨干网络，在CTW1500英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar) )，可以使用如下命令进行转换： + +```shell +python3 tools/export_model.py -c configs/det/det_r50_vd_dcn_fce_ctw.yml -o Global.pretrained_model=./det_r50_dcn_fce_ctw_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_fce +``` + +FCE文本检测模型推理，执行非弯曲文本检测，可以执行如下命令： + +```shell +python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_fce/" --det_algorithm="FCE" --det_fce_box_type=quad +``` + +可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下： + +![](../imgs_results/det_res_img_10_fce.jpg) + +如果想执行弯曲文本检测，可以执行如下命令： + +```shell +python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_fce/" --det_algorithm="FCE" --det_fce_box_type=poly +``` + +可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下： + +![](../imgs_results/det_res_img623_fce.jpg) + +**注意**：由于CTW1500数据集只有1000张训练图像，且主要针对英文场景，所以上述模型对中文文本图像检测效果会比较差。 + + +### 4.2 C++推理 + +由于后处理暂未使用CPP编写，FCE文本检测模型暂不支持CPP推理。 + + +### 4.3 Serving服务化部署 + +暂未支持 + + +### 4.4 更多推理部署 + +暂未支持 + + +## 5. FAQ + + +## 引用 + +```bibtex +@InProceedings{zhu2021fourier, + title={Fourier Contour Embedding for Arbitrary-Shaped Text Detection}, + author={Yiqin Zhu and Jianyong Chen and Lingyu Liang and Zhanghui Kuang and Lianwen Jin and Wayne Zhang}, + year={2021}, + booktitle = {CVPR} +} +``` diff --git a/doc/doc_ch/algorithm_det_psenet.md b/doc/doc_ch/algorithm_det_psenet.md index 90502a8a8ba39be37cac2326ac4f973bdd83ea29..58d8ccf97292f4e988861b618697fb0e7694fbab 100644 --- a/doc/doc_ch/algorithm_det_psenet.md +++ b/doc/doc_ch/algorithm_det_psenet.md @@ -36,7 +36,7 @@ ## 3. 模型训练、评估、预测 -上述PSENet模型使用ICDAR2015文本检测公开数据集训练得到，数据集下载可参考 [ocr_datasets](./dataset/ocr_datasets.md)。 +上述PSE模型使用ICDAR2015文本检测公开数据集训练得到，数据集下载可参考 [ocr_datasets](./dataset/ocr_datasets.md)。 数据下载完成后，请参考[文本检测训练教程](./detection.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的检测模型只需要**更换配置文件**即可。 diff --git a/doc/doc_en/algorithm_det_fcenet_en.md b/doc/doc_en/algorithm_det_fcenet_en.md new file mode 100644 index 0000000000000000000000000000000000000000..e15fb9a07ede3296d3de83c134457194d4639a1c --- /dev/null +++ b/doc/doc_en/algorithm_det_fcenet_en.md @@ -0,0 +1,104 @@ +# FCENet + +- [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: +> [Fourier Contour Embedding for Arbitrary-Shaped Text Detection](https://arxiv.org/abs/2104.10442) +> Yiqin Zhu and Jianyong Chen and Lingyu Liang and Zhanghui Kuang and Lianwen Jin and Wayne Zhang +> CVPR, 2021 + +On the CTW1500 dataset, the text detection result is as follows: + +|Model|Backbone|Configuration|Precision|Recall|Hmean|Download| +| --- | --- | --- | --- | --- | --- | --- | +| FCE | ResNet50_dcn | [configs/det/det_r50_vd_dcn_fce_ctw.yml](../../configs/det/det_r50_vd_dcn_fce_ctw.yml)| 88.39%|82.18%|85.27%|[trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar)| + + +## 2. Environment +Please prepare your environment referring to [prepare the environment](./environment_en.md) and [clone the repo](./clone_en.md). + + + +## 3. Model Training / Evaluation / Prediction + +The above FCE model is trained using the CTW1500 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md). + +After the data download is complete, please refer to [Text Detection Training Tutorial](./detection.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models. + + +## 4. Inference and Deployment + + +### 4.1 Python Inference +First, convert the model saved in the FCE text detection training process into an inference model. Taking the model based on the Resnet50_vd_dcn backbone network and trained on the CTW1500 English dataset as example ([model download link](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar)), you can use the following command to convert: + +```shell +python3 tools/export_model.py -c configs/det/det_r50_vd_dcn_fce_ctw.yml -o Global.pretrained_model=./det_r50_dcn_fce_ctw_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_fce +``` + +FCE text detection model inference, to perform non-curved text detection, you can run the following commands: + +```shell +python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_fce/" --det_algorithm="FCE" --det_fce_box_type=quad +``` + +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: + +![](../imgs_results/det_res_img_10_fce.jpg) + +If you want to perform curved text detection, you can execute the following command: + +```shell +python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_fce/" --det_algorithm="FCE" --det_fce_box_type=poly +``` + +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: + +![](../imgs_results/det_res_img623_fce.jpg) + +**Note**: Since the CTW1500 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. + + + +### 4.2 C++ Inference + +Since the post-processing is not written in CPP, the FCE text detection model does not support CPP inference. + + +### 4.3 Serving + +Not supported + + +### 4.4 More + +Not supported + + +## 5. FAQ + + +## Citation + +```bibtex +@InProceedings{zhu2021fourier, + title={Fourier Contour Embedding for Arbitrary-Shaped Text Detection}, + author={Yiqin Zhu and Jianyong Chen and Lingyu Liang and Zhanghui Kuang and Lianwen Jin and Wayne Zhang}, + year={2021}, + booktitle = {CVPR} +} +``` diff --git a/doc/doc_en/algorithm_det_psenet_en.md b/doc/doc_en/algorithm_det_psenet_en.md index aeec3d18760f28fe7263865782591563a1aa7ce4..d4cb3ea7d1e82a3f9c261c6e44cd6df6b0f6bf1e 100644 --- a/doc/doc_en/algorithm_det_psenet_en.md +++ b/doc/doc_en/algorithm_det_psenet_en.md @@ -37,7 +37,7 @@ Please prepare your environment referring to [prepare the environment](./environ ## 3. Model Training / Evaluation / Prediction -The above PSENet 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). +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). After the data download is complete, please refer to [Text Detection Training Tutorial](./detection.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models. diff --git a/doc/imgs_results/det_res_img623_fce.jpg b/doc/imgs_results/det_res_img623_fce.jpg new file mode 100644 index 0000000000000000000000000000000000000000..938ae4cabf32cf5f89519f81b33259b188ed494a Binary files /dev/null and b/doc/imgs_results/det_res_img623_fce.jpg differ diff --git a/doc/imgs_results/det_res_img_10_fce.jpg b/doc/imgs_results/det_res_img_10_fce.jpg new file mode 100644 index 0000000000000000000000000000000000000000..fb32950ffda29f3263ab8bddc445e7c71f7d2ee0 Binary files /dev/null and b/doc/imgs_results/det_res_img_10_fce.jpg differ