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:
- ac9a5bd242f4f67fa8bd4cebbe0629e91d1bb64adcceb7e7e03dbe787277cdf5
- Size of remote file:
- 1.65 GB
- SHA256:
- 445915de1c92c4ae37a45cc24c1bf8ab0ed6f8be400e1c11c1a6c547c3327710
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