Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
image-text
contrastive-learning
zero-shot
custom_code
Instructions to use google/tipsv2-b14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-b14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/tipsv2-b14", 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("google/tipsv2-b14", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Remove zero-shot segmentation snippet, link to GitHub for advanced use cases 8c6c309
Gabriele commited on
PCA visualization: add whitening and sigmoid d075dda
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Add navigation table linking all variants and DPT heads d0e7527
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Switch to bus image with matching classes 6dd2600
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Improve code examples: better classes, descriptive comments 670938a
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Use cat photo, add print statements to code examples 365a9aa
Gabriele commited on
Fix meta device crash: use pure Python for drop_path_rate f46d027
Gabriele commited on
Fix zero-shot segmentation section, use public example image 2cf893a
Gabriele commited on
Update README 6f6f8e8
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Align image_encoder.py drop_path_rate computation with upstream bc0c462
Gabriele commited on