# vedastr **Repository Path**: linkchainiii/vedastr ## Basic Information - **Project Name**: vedastr - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-04-25 - **Last Updated**: 2021-04-25 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## Introduction vedastr is an open source scene text recognition toolbox based on PyTorch. It is designed to be flexible in order to support rapid implementation and evaluation for scene text recognition task. ## Features - **Modular design**\ We decompose the scene text recognition framework into different components and one can easily construct a customized scene text recognition framework by combining different modules. - **Flexibility**\ vedastr is flexible enough to be able to easily change the components within a module. - **Module expansibility**\ It is easy to integrate a new module into the vedastr project. - **Support of multiple frameworks**\ The toolbox supports several popular scene text recognition framework, e.g., [CRNN](https://arxiv.org/abs/1507.05717), [TPS-ResNet-BiLSTM-Attention](https://github.com/clovaai/deep-text-recognition-benchmark), Transformer, etc. - **Good performance**\ We re-implement the best model in [deep-text-recognition-benchmark](https://github.com/clovaai/deep-text-recognition-benchmark) and get better average accuracy. What's more, we implement a simple baseline(ResNet-FC) and the performance is acceptable. ## License This project is released under [Apache 2.0 license](https://github.com/Media-Smart/vedastr/blob/master/LICENSE). ## Benchmark and model zoo Note: - We use [MJSynth(MJ)](http://www.robots.ox.ac.uk/~vgg/data/text/) and [SynthText(ST)](http://www.robots.ox.ac.uk/~vgg/data/scenetext/) as training data, and test the models on [IIIT5K_3000](http://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset), [SVT](http://vision.ucsd.edu/~kai/svt/), [IC03_867](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions), [IC13_1015](http://dagdata.cvc.uab.es/icdar2013competition/?ch=2&com=downloads), [IC15_2077](https://rrc.cvc.uab.es/?ch=4&com=downloads), SVTP, [CUTE80](http://cs-chan.com/downloads_CUTE80_dataset.html). You can find the datasets [below](https://github.com/Media-Smart/vedastr/tree/opencv-version#prepare-data). | MODEL|CASE SENSITIVE| IIIT5k_3000| SVT |IC03_867| IC13_1015| IC15_2077| SVTP| CUTE80| AVERAGE| |:----:|:----:| :----: | :----: |:----: |:----: |:----: |:----: |:----: | :----:| |[ResNet-CTC](https://drive.google.com/file/d/1gtTcc5kpVs_s5a6OR7eBh431Otk_-NrE/view?usp=sharing)| False|87.97 | 84.54 | 90.54 | 88.28 |67.99|72.71|77.08|81.58| |[ResNet-FC](https://drive.google.com/file/d/1OnUGdv9RFhFbQGXUUkWMcxUZg0mPV0kK/view?usp=sharing) | False|88.80 | 88.41 | 92.85| 90.34|72.32|79.38|76.74|84.24| |[TPS-ResNet-BiLSTM-Attention](https://drive.google.com/file/d/1YUOAU7xcrrsAtEqEGtI5ZD7eryP7Zr04/view?usp=sharing)| False|90.93 | 88.72 | 93.89| 92.12|76.41|80.31|79.51|86.49| |[Small-SATRN](https://drive.google.com/file/d/1bcKtEcYGIOehgPfGi_TqPkvrm6rjOUKR/view?usp=sharing)| False|91.97 | 88.10 | 94.81 | 93.50|75.64|83.88|80.90|87.19| TPS : [Spatial transformer network](https://arxiv.org/abs/1603.03915) Small-SATRN: [On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention](https://arxiv.org/abs/1910.04396), training phase is case sensitive while testing phase is case insensitive. AVERAGE : Average accuracy over all test datasets CASE SENSITIVE : If true, the output is case sensitive and contain common characters. If false, the output is not case sensetive and contains only numbers and letters. ## Installation ### Requirements - Linux - Python 3.6+ - PyTorch 1.4.0 or higher - CUDA 9.0 or higher We have tested the following versions of OS and softwares: - OS: Ubuntu 16.04.6 LTS - CUDA: 10.2 - Python 3.6.9 - Pytorch: 1.5.1 ### Install vedastr 1. Create a conda virtual environment and activate it. ```shell conda create -n vedastr python=3.6 -y conda activate vedastr ``` 2. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/), *e.g.*, ```shell conda install pytorch torchvision -c pytorch ``` 3. Clone the vedastr repository. ```shell git clone https://github.com/Media-Smart/vedastr.git cd vedastr vedastr_root=${PWD} ``` 4. Install dependencies. ```shell pip install -r requirements.txt ``` ## Prepare data 1. Download Lmdb data from [deep-text-recognition-benchmark](https://github.com/clovaai/deep-text-recognition-benchmark), which contains training, validation and evaluation data. **Note: we use the ST dataset released by [ASTER](https://github.com/ayumiymk/aster.pytorch#data-preparation).** 2. Make directory data as follows: ```shell cd ${vedastr_root} mkdir ${vedastr_root}/data ``` 3. Put the download LMDB data into this data directory, the structure of data directory will look like as follows: ```shell data └── data_lmdb_release ├── evaluation ├── training │   ├── MJ │   │   ├── MJ_test │   │   ├── MJ_train │   │   └── MJ_valid │   └── ST └── validation ``` ## Train 1. Config Modify some configuration accordingly in the config file like `configs/tps_resnet_bilstm_attn.py` 2. Run ```shell python tools/train.py configs/tps_resnet_bilstm_attn.py ``` Snapshots and logs will be generated at `vedastr/workdir` by default. ## Test 1. Config Modify some configuration accordingly in the config file like `configs/tps_resnet_bilstm_attn.py ` 2. Run ```shell python tools/test.py configs/tps_resnet_bilstm_attn.py checkpoint_path ``` ## Inference 1. Run ```shell python tools/inference.py configs/tps_resnet_bilstm_attn.py checkpoint_path img_path ``` ## Deploy 1. Install [volksdep](https://github.com/Media-Smart/volksdep) following the [official instructions](https://github.com/Media-Smart/volksdep#installation) 2. Benchmark (optional) ```python python tools/deploy/benchmark.py configs/resnet_ctc.py checkpoint_path image_file_path --calibration_images image_folder_path ``` More available arguments are detailed in [tools/deploy/benchmark.py](https://github.com/Media-Smart/vedastr/blob/master/tools/deploy/benchmark.py). The result of resnet_ctc is as follows(test device: Jetson AGX Xavier, CUDA:10.2): | framework | version | input shape | data type | throughput(FPS) | latency(ms) | | :-: | :-: | :-: | :-: | :-: | :-: | | pytorch | 1.5.0 | (1, 1, 32, 100) | fp32 | 64 | 15.81 | | tensorrt | 7.1.0.16 | (1, 1, 32, 100) | fp32 | 109 | 9.66 | | pytorch | 1.5.0 | (1, 1, 32, 100) | fp16 | 113 | 10.75 | | tensorrt | 7.1.0.16 | (1, 1, 32, 100) | fp16 | 308 | 3.55 | | tensorrt | 7.1.0.16 | (1, 1, 32, 100) | int8(entropy_2) | 449 | 2.38 | 3. Export model as ONNX or TensorRT engine format ```python python tools/deploy/export.py configs/resnet_ctc.py checkpoint_path image_file_path out_model_path ``` More available arguments are detailed in [tools/deploy/export.py](https://github.com/Media-Smart/vedastr/blob/master/tools/deploy/export.py). 4. Inference SDK You can refer to [FlexInfer](https://github.com/Media-Smart/flexinfer) for details. ## Citation If you use this toolbox or benchmark in your research, please cite this project. ``` @misc{2020vedastr, title = {vedastr: A Toolbox for Scene Text Recognition}, author = {Sun, Jun and Cai, Hongxiang and Xiong, Yichao}, url = {https://github.com/Media-Smart/vedastr}, year = {2020} } ``` ## Contact This repository is currently maintained by Jun Sun([@ChaseMonsterAway](https://github.com/ChaseMonsterAway)), Hongxiang Cai ([@hxcai](http://github.com/hxcai)), Yichao Xiong ([@mileistone](https://github.com/mileistone)).