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--- |
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datasets: |
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- agentsea/wave-ui |
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language: |
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- en |
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library_name: transformers |
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--- |
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# Paligemma WaveUI |
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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. |
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## Model Details |
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### Model Description |
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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. |
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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/). |
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- **Developed by:** https://agentsea.ai/ |
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- **Language(s) (NLP):** en |
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- **Finetuned from model:** https://huggingface.co/google/paligemma-3b-ft-widgetcap-448 |
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### Demo |
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You can find a **demo** for this model [here](https://huggingface.co/spaces/agentsea/paligemma-waveui). |
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## Notes |
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- The only task used in the fine-tune was the object detection task, so it might not perform well in other types of tasks. |
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## Usage |
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To start using this model, run the following: |
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```python |
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from transformers import AutoProcessor, PaliGemmaForConditionalGeneration |
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model = PaliGemmaForConditionalGeneration.from_pretrained("agentsea/paligemma-3b-ft-widgetcap-waveui-448").eval() |
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processor = AutoProcessor.from_pretrained("agentsea/paligemma-3b-ft-widgetcap-waveui-448") |
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``` |
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## Data |
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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. |
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## Evaluation |
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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: |
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- Gemini 1.5 Pro: 0.12 |
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- Claude 3.5 Sonnet: 0.05 |
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- GPT 4o: 0.05 |
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- **PaliGemma Widgetcap+WaveUI 448: 0.40** |
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- PaliGemma WaveUI 896: 0.49 |
