Instructions to use samim2024/clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samim2024/clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="samim2024/clip") 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("samim2024/clip") model = AutoModelForZeroShotImageClassification.from_pretrained("samim2024/clip") - Notebooks
- Google Colab
- Kaggle
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### The CLIP model was pretrained from openai/clip-vit-base-patch32 , to learn about what contributes to robustness in computer vision tasks.
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### The model has the ability to generalize to arbitrary image classification tasks in a zero-shot manner.
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Top predictions:
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Leggings and Salwar: 0.84%
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Women Kurta: 0.44%
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### The CLIP model was pretrained from openai/clip-vit-base-patch32 , to learn about what contributes to robustness in computer vision tasks.
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### The model has the ability to generalize to arbitrary image classification tasks in a zero-shot manner.
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