Image-to-Text
Transformers
Safetensors
English
blip
image-text-to-text
icon-description
image-captioning
🇪🇺 Region: EU
Instructions to use macpaw-research/blip-icon-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macpaw-research/blip-icon-captioning with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="macpaw-research/blip-icon-captioning")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("macpaw-research/blip-icon-captioning") model = AutoModelForMultimodalLM.from_pretrained("macpaw-research/blip-icon-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
added citation
Browse files
README.md
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If you use this model in your research, please cite the Screen2AX paper:
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```bibtex
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```
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---
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If you use this model in your research, please cite the Screen2AX paper:
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```bibtex
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@misc{muryn2025screen2axvisionbasedapproachautomatic,
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title={Screen2AX: Vision-Based Approach for Automatic macOS Accessibility Generation},
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author={Viktor Muryn and Marta Sumyk and Mariya Hirna and Sofiya Garkot and Maksym Shamrai},
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year={2025},
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eprint={2507.16704},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2507.16704},
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}
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```
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---
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