Instructions to use hf-internal-testing/tiny-random-DinatForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DinatForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-DinatForImageClassification") 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-DinatForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-DinatForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f2a41f997884437e949616d534e26002c11bdaad6f9783dffd4c2fe00a41df05
- Size of remote file:
- 324 kB
- SHA256:
- 2a9c34e723c7e2d49e0460b3c85f81b3dc977c70f57a070c667eb3c1f9e59ca3
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