Im2latex

🚀 im2latex

This model is a base VisionEncoderDecoderModel fine-tuned on a dataset for generating LaTeX formulas from images.

🚀 Quick Start

You can use the model directly with the transformers library:

from transformers import VisionEncoderDecoderModel, AutoTokenizer, AutoFeatureExtractor
import torch
from PIL import Image

# load model, tokenizer, and feature extractor
model = VisionEncoderDecoderModel.from_pretrained("DGurgurov/im2latex")
tokenizer = AutoTokenizer.from_pretrained("DGurgurov/im2latex")
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/swin-base-patch4-window7-224-in22k") # using the original feature extractor for now

# prepare an image
image = Image.open("path/to/your/image.png")
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values

# generate LaTeX formula
generated_ids = model.generate(pixel_values)
generated_texts = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)

print("Generated LaTeX formula:", generated_texts[0])

✨ Features

This model is a fine - tuned VisionEncoderDecoderModel for generating LaTeX formulas from images. It combines a Swin Transformer encoder and a GPT - 2 decoder, using PyTorch as the framework and DDP for training.

📦 Installation

The README doesn't provide specific installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import VisionEncoderDecoderModel, AutoTokenizer, AutoFeatureExtractor
import torch
from PIL import Image

# load model, tokenizer, and feature extractor
model = VisionEncoderDecoderModel.from_pretrained("DGurgurov/im2latex")
tokenizer = AutoTokenizer.from_pretrained("DGurgurov/im2latex")
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/swin-base-patch4-window7-224-in22k") # using the original feature extractor for now

# prepare an image
image = Image.open("path/to/your/image.png")
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values

# generate LaTeX formula
generated_ids = model.generate(pixel_values)
generated_texts = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)

print("Generated LaTeX formula:", generated_texts[0])

📚 Documentation

Model Details

Training Data

The data is taken from OleehyO/latex-formulas. The data was divided into 80:10:10 for train, val and test. The splits were made as follows:

dataset = load_dataset(OleehyO/latex-formulas, cleaned_formulas)
train_val_split = dataset["train"].train_test_split(test_size=0.2, seed=42)
train_ds = train_val_split["train"]
val_test_split = train_val_split["test"].train_test_split(test_size=0.5, seed=42)
val_ds = val_test_split["train"]
test_ds = val_test_split["test"]

Evaluation Metrics

The model was evaluated on a test set with the following results:

• Test Loss: 0.10
• Test BLEU Score: 0.67

Training Script

The training script for this model can be found in the following repository: GitHub

Citation

• If you use this work in your research, please cite our paper:

@misc{gurgurov2024imagetolatexconvertermathematicalformulas,
      title={Image-to-LaTeX Converter for Mathematical Formulas and Text}, 
      author={Daniil Gurgurov and Aleksey Morshnev},
      year={2024},
      eprint={2408.04015},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2408.04015}, 
}

📄 License

This project is under the [MIT] license.