Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
image-text
contrastive-learning
zero-shot
custom_code
Instructions to use google/tipsv1-l14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv1-l14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/tipsv1-l14", 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 AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("google/tipsv1-l14", trust_remote_code=True) model = AutoModel.from_pretrained("google/tipsv1-l14", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from google/tipsv1-l14: direct link, hf CLI and curl.
- Browser
- Download file 1.95 GB
-
https://huggingface.co/google/tipsv1-l14/resolve/main/model.safetensors
- Command line
-
hf download hf://google/tipsv1-l14/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/google/tipsv1-l14/resolve/main/model.safetensors
1.95 GB
- Xet hash:
- dbadac7c8f9b2b68fa27ef8b30323b9d184c99785bf03ada9a45a6f58b72d7da
- Size of remote file:
- 1.95 GB
- SHA256:
- 4e69fd95fb0ce676f793355d1a7a53fb90145608098a2a64e2af2a324e6c20bc
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