From 1ffde5851947838068b115e462a7863d49f6e53c Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Tue, 26 Apr 2022 06:58:07 +0000 Subject: [PATCH] refine db docs --- doc/doc_ch/algorithm_det_db.md | 15 ++++-- doc/doc_en/algorithm_det_db_en.md | 84 ++++++++++++++++++++++++++++++- 2 files changed, 93 insertions(+), 6 deletions(-) diff --git a/doc/doc_ch/algorithm_det_db.md b/doc/doc_ch/algorithm_det_db.md index fc887743..7f94ceae 100644 --- a/doc/doc_ch/algorithm_det_db.md +++ b/doc/doc_ch/algorithm_det_db.md @@ -47,13 +47,13 @@ ### 4.1 Python推理 首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_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_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_db ``` DB文本检测模型推理，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_db/" ``` @@ -65,15 +65,20 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_ ### 4.2 C++推理 -敬请期待 + +准备好推理模型后，参考[cpp infer](../../deploy/cpp_infer/)教程进行操作即可。 ### 4.3 Serving服务化部署 -敬请期待 + +准备好推理模型后，参考[pdserving](../../deploy/pdserving/)教程进行Serving服务化部署，包括Python Serving和C++ Serving两种模式。 ### 4.4 更多推理部署 -敬请期待 + +DB模型还支持以下推理部署方式： + +- Paddle2ONNX推理：准备好推理模型后，参考[paddle2onnx](../../deploy/paddle2onnx/)教程操作。 ## 5. FAQ diff --git a/doc/doc_en/algorithm_det_db_en.md b/doc/doc_en/algorithm_det_db_en.md index 40ba022f..b387a8ec 100644 --- a/doc/doc_en/algorithm_det_db_en.md +++ b/doc/doc_en/algorithm_det_db_en.md @@ -14,4 +14,86 @@ - [5. FAQ](#5) -## 1. Introduction \ No newline at end of file +## 1. Introduction + +Paper: +> [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/abs/1911.08947) +> Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang +> AAAI, 2020 + +On the ICDAR2015 dataset, the text detection result is as follows: + +|Model|Backbone|Configuration|Precision|Recall|Hmean|Download| +| --- | --- | --- | --- | --- | --- | --- | +|DB|ResNet50_vd|configs/det/det_r50_vd_db.yml|86.41%|78.72%|82.38%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)| +|DB|MobileNetV3|configs/det/det_mv3_db.yml|77.29%|73.08%|75.12%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_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 + +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. + + +## 4. Inference and Deployment + + +### 4.1 Python Inference +First, convert the model saved in the DB 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_v2.0/en/det_r50_vd_db_v2.0_train.tar)), you can use the following command to convert: + +```shell +python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_db +``` + +DB text detection model inference, you can execute the following command: + +```shell +python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/" +``` + +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_db.jpg) + +**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 text images. + + + +### 4.2 C++ Inference + +With the inference model prepared, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial for C++ inference. + + +### 4.3 Serving + +With the inference model prepared, refer to the [pdserving](../../deploy/pdserving/) tutorial for service deployment by Paddle Serving. + + +### 4.4 More + +More deployment schemes supported for DB: + +- Paddle2ONNX: with the inference model prepared, please refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial. + + +## 5. FAQ + + +## Citation + +```bibtex +@inproceedings{liao2020real, + title={Real-time scene text detection with differentiable binarization}, + author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang}, + booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, + volume={34}, + number={07}, + pages={11474--11481}, + year={2020} +} +``` \ No newline at end of file -- GitLab