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")# 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
- Xet hash:
- b82dbf32b68d909d86b6c8e88132f9b8e615f2a5fdf946df253fefb30da75b86
- Size of remote file:
- 318 kB
- SHA256:
- dbe6f0bb4f4254e8abd0c81b6008750bf2e1896f4dbb79f50e1aa878d96a1728
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