Instructions to use Ramos-Ramos/vicreg-resnet-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ramos-Ramos/vicreg-resnet-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Ramos-Ramos/vicreg-resnet-50")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("Ramos-Ramos/vicreg-resnet-50") model = AutoModel.from_pretrained("Ramos-Ramos/vicreg-resnet-50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ramos-Ramos/vicreg-resnet-50: direct link, hf CLI and curl.
- Browser
- Download file 94.4 MB
-
https://huggingface.co/Ramos-Ramos/vicreg-resnet-50/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ramos-Ramos/vicreg-resnet-50/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ramos-Ramos/vicreg-resnet-50/resolve/main/pytorch_model.bin
94.4 MB
- Xet hash:
- 5f9fce29e0f614fafcc24f548361b5b5b37c0399519be7c135574a3b5b1bde1a
- Size of remote file:
- 94.4 MB
- SHA256:
- f6f88d5464d614be5771739cbd3a43426f4c19eff23e7ad5e3cc6f0f3a93b155
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