133 lines
3.6 KiB
Markdown
133 lines
3.6 KiB
Markdown
# Real-time Scene Text Detection with Differentiable Binarization
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**note**: some code is inherited from [WenmuZhou/DBNet.pytorch](https://github.com/WenmuZhou/DBNet.pytorch)
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[中文解读](https://zhuanlan.zhihu.com/p/94677957)
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## update
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2020-06-07: 添加灰度图训练，训练灰度图时需要在配置里移除`dataset.args.transforms.Normalize`
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## Install Using Conda
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```
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conda env create -f environment.yml
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git clone https://github.com/WenmuZhou/DBNet.paddle.git
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cd DBNet.paddle/
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```
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or
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## Install Manually
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```bash
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conda create -n dbnet python=3.6
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conda activate dbnet
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conda install ipython pip
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# python dependencies
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pip install -r requirement.txt
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# clone repo
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git clone https://github.com/WenmuZhou/DBNet.paddle.git
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cd DBNet.paddle/
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```
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## Requirements
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* paddlepaddle 2.4+
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## Download
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TBD
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## Data Preparation
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Training data: prepare a text `train.txt` in the following format, use '\t' as a separator
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```
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./datasets/train/img/001.jpg ./datasets/train/gt/001.txt
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```
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Validation data: prepare a text `test.txt` in the following format, use '\t' as a separator
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```
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./datasets/test/img/001.jpg ./datasets/test/gt/001.txt
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```
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- Store images in the `img` folder
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- Store groundtruth in the `gt` folder
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The groundtruth can be `.txt` files, with the following format:
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```
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x1, y1, x2, y2, x3, y3, x4, y4, annotation
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```
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## Train
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1. config the `dataset['train']['dataset'['data_path']'`,`dataset['validate']['dataset'['data_path']`in [config/icdar2015_resnet18_fpn_DBhead_polyLR.yaml](cconfig/icdar2015_resnet18_fpn_DBhead_polyLR.yaml)
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* . single gpu train
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```bash
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bash singlel_gpu_train.sh
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```
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* . Multi-gpu training
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```bash
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bash multi_gpu_train.sh
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```
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## Test
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[eval.py](tools/eval.py) is used to test model on test dataset
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1. config `model_path` in [eval.sh](eval.sh)
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2. use following script to test
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```bash
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bash eval.sh
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```
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## Predict
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[predict.py](tools/predict.py) Can be used to inference on all images in a folder
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1. config `model_path`,`input_folder`,`output_folder` in [predict.sh](predict.sh)
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2. use following script to predict
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```
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bash predict.sh
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```
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You can change the `model_path` in the `predict.sh` file to your model location.
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tips: if result is not good, you can change `thre` in [predict.sh](predict.sh)
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## Export Model
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[export_model.py](tools/export_model.py) Can be used to inference on all images in a folder
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use following script to export inference model
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```
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python tools/export_model.py --config_file config/icdar2015_resnet50_FPN_DBhead_polyLR.yaml -o trainer.resume_checkpoint=model_best.pth trainer.output_dir=output/infer
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```
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## Paddle Inference infer
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[infer.py](tools/infer.py) Can be used to inference on all images in a folder
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use following script to export inference model
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```
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python tools/infer.py --model-dir=output/infer/ --img-path imgs/paper/db.jpg
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```
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<h2 id="Performance">Performance</h2>
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### [ICDAR 2015](http://rrc.cvc.uab.es/?ch=4)
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only train on ICDAR2015 dataset
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| Method | image size (short size) |learning rate | Precision (%) | Recall (%) | F-measure (%) | FPS |
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|:--------------------------:|:-------:|:--------:|:--------:|:------------:|:---------------:|:-----:|
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| ImageNet-resnet50-FPN-DBHead（torch） |736 |1e-3|90.19 | 78.14 | 83.88 | 27 |
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| ImageNet-resnet50-FPN-DBHead（paddle） |736 |1e-3| 89.47 | 79.03 | 83.92 | 27 |
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| ImageNet-resnet50-FPN-DBHead（paddle_amp） |736 |1e-3| 88.62 | 79.95 | 84.06 | 27 |
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### examples
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TBD
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### reference
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1. https://arxiv.org/pdf/1911.08947.pdf
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2. https://github.com/WenmuZhou/DBNet.pytorch
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**If this repository helps you，please star it. Thanks.**
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