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