Instructions to use internetoftim/dinov2-base-eurosat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internetoftim/dinov2-base-eurosat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="internetoftim/dinov2-base-eurosat") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("internetoftim/dinov2-base-eurosat") model = AutoModelForImageClassification.from_pretrained("internetoftim/dinov2-base-eurosat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from internetoftim/dinov2-base-eurosat: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/internetoftim/dinov2-base-eurosat/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://internetoftim/dinov2-base-eurosat/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/internetoftim/dinov2-base-eurosat/resolve/main/pytorch_model.bin
346 MB
- Xet hash:
- 7fe5d81d8c1585f36392cb935ee73b621117f61b6758778e12dd39808b894360
- Size of remote file:
- 346 MB
- SHA256:
- 288c2fab95e0980b1223cc6c9c6e2a30ee8944f279470af9c9270fa575a63b01
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