README.md

---
datasets:
- agentsea/wave-ui
language:
- en
library_name: transformers
---

# Paligemma WaveUI


Transformers [PaliGemma 3B 448-res weights](https://huggingface.co/google/paligemma-3b-pt-448), fine-tuned on the [WaveUI](https://huggingface.co/datasets/agentsea/wave-ui) dataset for object-detection.

## Model Details

### Model Description

This fine-tune was done atop of the [Paligemma 448 Widgetcap](https://huggingface.co/google/paligemma-3b-ft-widgetcap-448) model, using the [WaveUI](https://huggingface.co/datasets/agentsea/wave-ui) dataset, which contains ~80k examples of labeled UI elements.

The fine-tune was done for the object detection task. Specifically, this model aims to perform well at UI element detection, as part of a wider effort to enable our open-source toolkit for building agents at [AgentSea](https://www.agentsea.ai/).

- **Developed by:** https://agentsea.ai/
- **Language(s) (NLP):** en
- **Finetuned from model:** https://huggingface.co/google/paligemma-3b-ft-widgetcap-448

### Demo

You can find a **demo** for this model [here](https://huggingface.co/spaces/agentsea/paligemma-waveui).

## Notes

- The only task used in the fine-tune was the object detection task, so it might not perform well in other types of tasks.
  
## Usage

To start using this model, run the following:

```python
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration

model = PaliGemmaForConditionalGeneration.from_pretrained("agentsea/paligemma-3b-ft-widgetcap-waveui-448").eval()
processor = AutoProcessor.from_pretrained("agentsea/paligemma-3b-ft-widgetcap-waveui-448")
```

## Data

We used the [WaveUI](https://huggingface.co/datasets/agentsea/wave-ui) dataset for this fine-tune. Before using it, we preprocessed the data to use the Paligemma bounding-box format.
 

## Evaluation

We calculated the mean IoU over 1024 examples of the test set using 3 different closed-source models: Gemini 1.5 Pro, Claude 3.5 Sonnet and GPT 4o. We also ran this same calculation using the PaliGemma WaveUI fine-tunes. We obtained the following values:

- Gemini 1.5 Pro: 0.12
- Claude 3.5 Sonnet: 0.05
- GPT 4o: 0.05
- **PaliGemma Widgetcap+WaveUI 448: 0.40**
- PaliGemma WaveUI 896: 0.49