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This respository implement the Seq2Seq Image to Latex architecture from paper “Image to Latex.” of Genthial, Guillaume. (2017).

This structure is based on Seq2Seq architecture, it use one Convolutional Encoder and one RNN Decoder.

• Convolution (only)
• Convolution with Row Encoder (BiLSTM)
• Convolution with Batch Norm
• ResNet 18 with Row Encoder (BiLSTM)
• ResNet 18 (only)

• https://www.kaggle.com/datasets/shahrukhkhan/im2latex100k
• https://www.kaggle.com/datasets/tuannguyenvananh/im2latex-sorted-by-size

• https://www.kaggle.com/datasets/rvente/im2latex170k
• https://www.kaggle.com/datasets/tuannguyenvananh/im2latex-170k-meta-data

• wandb login <key>

python main.py --batch-size 2 --data-path C:\Users\nvatu\OneDrive\Desktop\dataset5\dataset5 --img-path C:\Users\nvatu\OneDrive\Desktop\dataset5\dataset5\formula_images --dataset 170k --val --decode-type beamsearch

• ResNet Row Encoder