algorithm_rec_vitstr_en.md

ViTSTR

• 1. Introduction
• 2. Environment
• 3. Model Training / Evaluation / Prediction
  • 3.1 Training
  • 3.2 Evaluation
  • 3.3 Prediction
• 4. Inference and Deployment
  • 4.1 Python Inference
  • 4.2 C++ Inference
  • 4.3 Serving
  • 4.4 More
• 5. FAQ

1. Introduction

Paper:

Vision Transformer for Fast and Efficient Scene Text Recognition 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	79.82%	trained model

2. Environment

Please refer to "Environment Preparation" to configure the PaddleOCR environment, and refer to "Project Clone" to clone the project code.

3. Model Training / Evaluation / Prediction

Please refer to Text Recognition Tutorial. 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) ), 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'

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:

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

@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}
}