Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
contrastive-learning
zero-shot
custom_code
Instructions to use nebulette/tipsv2-b14-vision-module with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nebulette/tipsv2-b14-vision-module with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="nebulette/tipsv2-b14-vision-module", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nebulette/tipsv2-b14-vision-module", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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```python
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from tipsv2_b14_vision_module import TIPSv2ImageModel
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model = TIPSv2ImageModel.from_pretrained("
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model.eval()
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```
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```python
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from tipsv2_b14_vision_module import TIPSv2ImageModel
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model = TIPSv2ImageModel.from_pretrained("nebulette/tipsv2-b14-vision-module", trust_remote_code=True)
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model.eval()
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```
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