Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing
Paper • 2303.00915 • Published • 6
# Load model directly
from transformers import AutoImageProcessor, AutoModel
processor = AutoImageProcessor.from_pretrained("ikim-uk-essen/BiomedCLIP_ViT_patch16_224")
model = AutoModel.from_pretrained("ikim-uk-essen/BiomedCLIP_ViT_patch16_224", device_map="auto")Quick Links
This repository contains Microsoft's BiomedCLIP converted to the Hugging Face format to simplify access.
All credits go to BiomedCLIP:
@misc{https://doi.org/10.48550/arXiv.2303.00915,
doi = {10.48550/ARXIV.2303.00915},
url = {https://arxiv.org/abs/2303.00915},
author = {Zhang, Sheng and Xu, Yanbo and Usuyama, Naoto and Bagga, Jaspreet and Tinn, Robert and Preston, Sam and Rao, Rajesh and Wei, Mu and Valluri, Naveen and Wong, Cliff and Lungren, Matthew and Naumann, Tristan and Poon, Hoifung},
title = {Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing},
publisher = {arXiv},
year = {2023},
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="ikim-uk-essen/BiomedCLIP_ViT_patch16_224")