Zero-Shot Image Classification
Transformers
English
medical
multimodal
vision-language pre-training
chest x-ray
Instructions to use pykale/MeDSLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pykale/MeDSLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="pykale/MeDSLIP") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pykale/MeDSLIP", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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# MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained Alignment
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## Introduction:
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```
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## Contact
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If you have any question, please feel free to contact winslow.fan@outlook.com.
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---
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license: cc-by-nc-4.0
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language:
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- en
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pipeline_tag: zero-shot-image-classification
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tags:
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- medical
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- multimodal
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- vision-language pre-training
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- chest x-ray
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---
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# MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained Alignment
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## Introduction:
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```
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## Contact
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If you have any question, please feel free to contact winslow.fan@outlook.com.
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