Instructions to use tomg-group-umd/CSD-ViT-L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tomg-group-umd/CSD-ViT-L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="tomg-group-umd/CSD-ViT-L")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tomg-group-umd/CSD-ViT-L", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d338dbfa732a6423999d212479678fb8836ec604e324d8e19f058e8f23aa4f0
- Size of remote file:
- 2.44 GB
- SHA256:
- 40e92fad63a361b8136100cd234c42d401ef9b34ff1748234318929ebcc7e7a1
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