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:
- e219fdeb7a134c872b7c09471638befbc18d60b4afe1d11dd6ac8d6237d5986e
- Size of remote file:
- 108 kB
- SHA256:
- 26996c71529f0c7c148933c3e4109edb44ec19103b6c298429b8fb30ad5d8b64
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