README.md

---
library_name: transformers
license: mit
datasets:
- SpursgoZmy/MMTab
- apoidea/pubtabnet-html
language:
- en
base_model: google/pix2struct-base
---

# pix2struct-base-table2html

*Turn table images into HTML!*


## Demo app

Try the [demo app]() which contains both table detection and recognition!


## About

This model takes an image of a table and outputs HTML - the model parses the image and performs optical character recognition (OCR) and structure recognition to HTML format. 

The model expects an image containing only a table. If the table is embedded in a document, first use a table detection model to extract it.

The model is finetuned from [Pix2Struct base model](https://huggingface.co/google/pix2struct-base) using a max_patch_length of 1024 and max generation length of 1024. The max_patch_length should likely not be changed for inference but the generation length can be changed.

The model has been trained using two datasets: [MMTab](https://huggingface.co/datasets/SpursgoZmy/MMTab) and [PubTabNet](https://huggingface.co/datasets/apoidea/pubtabnet-html).

## Usage

Below is a complete example of loading the model and performing inference on an example table image (example from the [MMTab dataset](https://huggingface.co/datasets/SpursgoZmy/MMTab)):

```python
import torch
from transformers import AutoProcessor, Pix2StructForConditionalGeneration
from PIL import Image
import requests
from io import BytesIO

# Load model and processor
device = "cuda" if torch.cuda.is_available() else "cpu"
processor = AutoProcessor.from_pretrained("pix2struct-base-table2html")
model = Pix2StructForConditionalGeneration.from_pretrained("pix2struct-base-table2html")
model.to(device)
model.eval()

# Load example image from URL
url = "https://example.com/path_to_table_image.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content))

# Run model inference
encoding = processor(image, return_tensors="pt", max_patches=1024)
with torch.inference_mode():
    flattened_patches = encoding.pop("flattened_patches").to(device)
    attention_mask = encoding.pop("attention_mask").to(device)
    predictions = model.generate(flattened_patches=flattened_patches, attention_mask=attention_mask, max_new_tokens=1024)

predictions_decoded = processor.tokenizer.batch_decode(predictions, skip_special_tokens=True)

# Show predictions as text
print(predictions_decoded[0])
```