Instructions to use LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained") model = AutoModelForZeroShotImageClassification.from_pretrained("LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5af4528b44533b11e7498bd2101d83071f89fd890529aeb298069ea93700f053
- Size of remote file:
- 4.96 GB
- SHA256:
- cdbdef39102b47d087377d308c42ca94d44d72a1fea81525f8df9f059c4a58ec
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