Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
image-text
contrastive-learning
zero-shot
custom_code
Instructions to use invincible-jha/tipsv2-g14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use invincible-jha/tipsv2-g14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="invincible-jha/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("invincible-jha/tipsv2-g14", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32bad4fc5872399a126dde7719084caa56b1f33e10c045036fc90e7df46d448c
- Size of remote file:
- 732 kB
- SHA256:
- 4c40e7723348d5d9a3d3c2bdcec5120d97fb29edfe1bf118b4494bce02fc7624
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