Instructions to use hf-internal-testing/tiny-random-resnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-resnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-resnet") 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-resnet") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-resnet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe9479d708f8ce884962cadf1d7e625b5aafbb278b698fc73c4061eead99ad5d
- Size of remote file:
- 399 kB
- SHA256:
- 7e126c7921476a0e02a4aa614166508c9a4dcc15f4c6b3a7503ec8f324dd2e7c
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