agentsea/paligemma-3b-ft-widgetcap-waveui-448

Paligemma WaveUI

Transformers PaliGemma 3B 448-res weights, fine-tuned on the WaveUI dataset for object-detection.

Model Details

Model Description

This fine-tune was done atop of the Paligemma 448 Widgetcap model, using the WaveUI 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.

• 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.

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:

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 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