Instructions to use hf-tiny-model-private/tiny-random-DinatForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-DinatForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/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-tiny-model-private/tiny-random-DinatForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-DinatForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46b81c6f8806e0ab50af4141b9af4f5ce8473411eee6568dd49bf02a2eb68651
- Size of remote file:
- 324 kB
- SHA256:
- 75f7211ea53c98e526e2f9de0978e7eb62f42f4527af601d7be6b1175ecf9ab4
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