Zero-Shot Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
vision-text-dual-encoder
feature-extraction
clip
vision
medical
bert
Instructions to use kaveh/rclip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaveh/rclip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="kaveh/rclip") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("kaveh/rclip") model = AutoModel.from_pretrained("kaveh/rclip") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 152f4267ced9e466fb425ee3b247408e7326ccb3091c4e71c091839c34c328c6
- Size of remote file:
- 4.09 kB
- SHA256:
- 5774ed6a6ee7d12e490235aaa201c6682bceb991af75ae978177e58f9bbfb1a7
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