Instructions to use prithivMLmods/MetaCLIP-2-Cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/MetaCLIP-2-Cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/MetaCLIP-2-Cifar10") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/MetaCLIP-2-Cifar10") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/MetaCLIP-2-Cifar10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13a7ba684aad4c0b57c5e4a5b0ff05d59abdf86441d68ec2cea1d3d44eea0992
- Size of remote file:
- 174 MB
- SHA256:
- 08e83a46af7737bff566b2f7f8b4fd87001b4bea72c17045891ca059b70fc464
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