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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 577494a636f2358c2b0a1c9aa178c761e844282b77dd6d3e4a2add79ae5d53ba
- Size of remote file:
- 93 MB
- SHA256:
- 3d3d3c13f1d526feeca58db839fb94971f394039440c14e639cbdca9aa3de419
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