Instructions to use hf-internal-testing/tiny-random-ResNetForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ResNetForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-ResNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ResNetForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4cc6bcb237f3c65f8e72d029e3eff13a348f481b6c6ea9182d9db065d49290b7
- Size of remote file:
- 108 kB
- SHA256:
- f14e4d22b8855ecf6cfe723d664020af545f104056b0a9358971d61ddd6ff850
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