Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
image-text
contrastive-learning
zero-shot
custom_code
Instructions to use google/tipsv2-so400m14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-so400m14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/tipsv2-so400m14", 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-so400m14", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Remove zero-shot segmentation snippet, link to GitHub for advanced use cases 6b864bd
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PCA visualization: add whitening and sigmoid 190b924
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Add navigation table linking all variants and DPT heads 54de1d9
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Switch to bus image with matching classes 3df09f9
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Improve code examples: better classes, descriptive comments 828f85a
Gabriele commited on
Use cat photo, add print statements to code examples 4407365
Gabriele commited on
Fix meta device crash: use pure Python for drop_path_rate 05bf02c
Gabriele commited on
Fix zero-shot segmentation section, use public example image 2bad776
Gabriele commited on
Update README 3d0bb08
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Align image_encoder.py drop_path_rate computation with upstream 99e5c3a
Gabriele commited on