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"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/tipsv2-g14", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Remove zero-shot segmentation snippet, link to GitHub for advanced use cases 18d5063
Gabriele commited on
PCA visualization: add whitening and sigmoid 4842da4
Gabriele commited on
Add navigation table linking all variants and DPT heads cf63d64
Gabriele commited on
Switch to bus image with matching classes be4b3bf
Gabriele commited on
Improve code examples: better classes, descriptive comments d867321
Gabriele commited on
Use cat photo, add print statements to code examples c1eb665
Gabriele commited on
Fix meta device crash: use pure Python for drop_path_rate 1785a7e
Gabriele commited on
Fix zero-shot segmentation section, use public example image ee929f8
Gabriele commited on
Update README c7d0623
Gabriele commited on
Align image_encoder.py drop_path_rate computation with upstream 8e6f865
Gabriele commited on