Instructions to use Citaman/VeCLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Citaman/VeCLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Citaman/VeCLIP") 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, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Citaman/VeCLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("Citaman/VeCLIP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Contribution Guide
Thanks for your interest in contributing. This project was released to accompany a research paper for purposes of reproducibility, and beyond its publication there are limited plans for future development of the repository.
While we welcome new pull requests and issues please note that our response may be limited. Forks and out-of-tree improvements are strongly encouraged.
Before you get started
By submitting a pull request, you represent that you have the right to license your contribution to Apple and the community, and agree by submitting the patch that your contributions are licensed under the LICENSE.
We ask that all community members read and observe our Code of Conduct.