Zero-Shot Image Classification
Transformers
Safetensors
English
clip
noisyclip
diffusion
latent-alignment
text-to-image
vision
Instructions to use asiimo/noisyclip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asiimo/noisyclip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="asiimo/noisyclip") 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("asiimo/noisyclip") model = AutoModelForZeroShotImageClassification.from_pretrained("asiimo/noisyclip") - Notebooks
- Google Colab
- Kaggle
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- **Architecture:** `CLIPModel` (ViT-L/14, fully compatible with 🤗 Transformers)
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- **Base model:** `openai/clip-vit-large-patch14`
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- **Fine-tuned components:** vision encoder only (text encoder + projections frozen)
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- **Developed by:** NOVA LINCS, NOVA School of Science and Technology
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- **License:** MIT
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## Intended Uses
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- **Architecture:** `CLIPModel` (ViT-L/14, fully compatible with 🤗 Transformers)
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- **Base model:** `openai/clip-vit-large-patch14`
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- **Fine-tuned components:** vision encoder only (text encoder + projections frozen)
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- **Developed by:** NOVA LINCS, NOVA School of Science and Technology + Google Research
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- **License:** MIT
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## Intended Uses
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