Instructions to use hf-tiny-model-private/tiny-random-ResNetForImageClassification 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-ResNetForImageClassification 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-ResNetForImageClassification") 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-ResNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-ResNetForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fed37dccbba3256d36ce5d64f05c3a8bd3bfa144cb632b3141619740c741d97e
- Size of remote file:
- 84.9 kB
- SHA256:
- f996a602fcc8f2aac950dbc89d4d11f2d3d68112d96e031c82a7029ff3ce74d7
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