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/2bb17a9d87e197b048790a3ccdf5c1f093eaf57d/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Dinov2ForImageClassification@2bb17a9d87e197b048790a3ccdf5c1f093eaf57d/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Dinov2ForImageClassification/resolve/2bb17a9d87e197b048790a3ccdf5c1f093eaf57d/pytorch_model.bin
318 kB
- Xet hash:
- ca199df1fd9dd158ea182907fdc36548a5fc9e9888f69d304a99444be5aab738
- Size of remote file:
- 318 kB
- SHA256:
- 740f0ba6dc561bab8a6075e94da36399d425905d8c34568477bad741c68c2360
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