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:
- 5241205a1d889cccda899a3dd5c86f6807252a5656fd9abbc9601bb8c9ea958b
- Size of remote file:
- 318 kB
- SHA256:
- 3e7446e662435771301c1b7d13f48bb770fb5e63f9ed7767650c4be7c955e734
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