Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing
Paper β’ 2303.00915 β’ Published β’ 6
How to use ikim-uk-essen/BiomedCLIP_ViT_patch16_224 with Transformers:
# 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") # 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")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},
}