Instructions to use hf-tiny-model-private/tiny-random-CLIPSegModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-CLIPSegModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hf-tiny-model-private/tiny-random-CLIPSegModel") 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("hf-tiny-model-private/tiny-random-CLIPSegModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-CLIPSegModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 110c14ad415b211978decfeea13a6efa68e3df293ee30c67e6ab2a3a50ecc2bd
- Size of remote file:
- 541 kB
- SHA256:
- 5925764e60c09372a3631915a7d76e5cd8c871918fd8fead2ddeec67d0a54f28
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