Instructions to use hf-internal-testing/tiny-random-Dinov2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Dinov2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-Dinov2ForImageClassification") 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("hf-internal-testing/tiny-random-Dinov2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-Dinov2ForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-Dinov2ForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 318 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-Dinov2ForImageClassification/resolve/deb227883fe0766148bb3bbb24b7a1009cc70da8/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Dinov2ForImageClassification@deb227883fe0766148bb3bbb24b7a1009cc70da8/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Dinov2ForImageClassification/resolve/deb227883fe0766148bb3bbb24b7a1009cc70da8/pytorch_model.bin
318 kB
- Xet hash:
- 6f18dd5da5b42e0d1e9bf3d01b385d404abcec6dea2aa764b48bb29dcb8b1d09
- Size of remote file:
- 318 kB
- SHA256:
- cac6356180e5a857a3da9acaca1134d092d46b42880dca367a18589492bbbe79
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