Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
image-text
contrastive-learning
zero-shot
custom_code
Instructions to use google/tipsv2-g14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-g14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/tipsv2-g14", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("google/tipsv2-g14", trust_remote_code=True) model = AutoModel.from_pretrained("google/tipsv2-g14", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Use relative imports for sibling modules (fixes local loading, save_pretrained, pickling)
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Support sentencepiece 0.2.2 in text tokenizer
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Add arXiv tag to link the paper
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Add arXiv paper link to citation
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Update files for transformers integration (#1)
- Update files for transformers integration (0b1a9035a7d699523bf5b735f9c17a7bd5333ddf) - Update configuration_tips.py (f154ae5fe8a6487830b372c79949b964b68289e7) Co-authored-by: guarin <guarin@users.noreply.huggingface.co>54cb39b
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Fix broken image URL and add missing .cpu() in code snippets
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Remove zero-shot segmentation snippet, link to GitHub for advanced use cases
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PCA visualization: add whitening and sigmoid
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Add navigation table linking all variants and DPT heads
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Switch to bus image with matching classes
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Improve code examples: better classes, descriptive comments
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Use cat photo, add print statements to code examples
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Fix meta device crash: use pure Python for drop_path_rate
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Fix zero-shot segmentation section, use public example image
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Update README
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Align image_encoder.py drop_path_rate computation with upstream
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