Instructions to use Shubhamai/tiny-random-clip-zero-shot-image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shubhamai/tiny-random-clip-zero-shot-image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Shubhamai/tiny-random-clip-zero-shot-image-classification") 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, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Shubhamai/tiny-random-clip-zero-shot-image-classification") model = AutoModelForZeroShotImageClassification.from_pretrained("Shubhamai/tiny-random-clip-zero-shot-image-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c3a23f13d1d58e6ee634baa7559ddda4e1513add877b0c89036fcfe5cd968c6
- Size of remote file:
- 403 kB
- SHA256:
- eb9f86d8ed768064b9ab0cb787d37069b4a07aba1be67489152d647ff6b0b5b8
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