# ViTSTR - [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: > [Vision Transformer for Fast and Efficient Scene Text Recognition](https://arxiv.org/abs/2105.08582) > Rowel Atienza > ICDAR, 2021 Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows: |Model|Backbone|config|Acc|Download link| | --- | --- | --- | --- | --- | |ViTSTR|ViTSTR|[rec_vitstr_none_ce.yml](../../configs/rec/rec_vitstr_none_ce.yml)|79.82%|[训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar)| ## 2. Environment Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. ## 3. Model Training / Evaluation / Prediction Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Training: Specifically, after the data preparation is completed, the training can be started. The training command is as follows: ``` #Single GPU training (long training period, not recommended) python3 tools/train.py -c configs/rec/rec_vitstr_none_ce.yml #Multi GPU training, specify the gpu number through the --gpus parameter python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_vitstr_none_ce.yml ``` Evaluation: ``` # GPU evaluation python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_vitstr_none_ce.yml -o Global.pretrained_model={path/to/weights}/best_accuracy ``` Prediction: ``` # The configuration file used for prediction must match the training python3 tools/infer_rec.py -c configs/rec/rec_vitstr_none_ce.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_vitstr_none_ce_train/best_accuracy ``` ## 4. Inference and Deployment ### 4.1 Python Inference First, the model saved during the ViTSTR text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar)) ), you can use the following command to convert: ``` python3 tools/export_model.py -c configs/rec/rec_vitstr_none_ce.yml -o Global.pretrained_model=./rec_vitstr_none_ce_train/best_accuracy Global.save_inference_dir=./inference/rec_vitstr ``` **Note:** - If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file. - If you modified the input size during training, please modify the `infer_shape` corresponding to ViTSTR in the `tools/export_model.py` file. After the conversion is successful, there are three files in the directory: ``` /inference/rec_vitstr/ ├── inference.pdiparams ├── inference.pdiparams.info └── inference.pdmodel ``` For ViTSTR text recognition model inference, the following commands can be executed: ``` python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_vitstr/' --rec_algorithm='ViTSTR' --rec_image_shape='1,224,224' --rec_char_dict_path='./ppocr/utils/EN_symbol_dict.txt' ``` ![](../imgs_words_en/word_10.png) After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows: The result is as follows: ```shell Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9998350143432617) ``` ### 4.2 C++ Inference Not supported ### 4.3 Serving Not supported ### 4.4 More Not supported ## 5. FAQ 1. In the `ViTSTR` paper, using pre-trained weights on ImageNet1k for initial training, we did not use pre-trained weights in training, and the final accuracy did not change or even improved. ## Citation ```bibtex @article{Atienza2021ViTSTR, title = {Vision Transformer for Fast and Efficient Scene Text Recognition}, author = {Rowel Atienza}, booktitle = {ICDAR}, year = {2021}, url = {https://arxiv.org/abs/2105.08582} } ```