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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f310286125f90ba780033b98285a8d1f0525f6e6cfbb1315751495c42affe1c
- Size of remote file:
- 203 kB
- SHA256:
- a359d4e86e837eb5751aed776669c747d91530fb7fb2ca8c29a5aeef4fefc77d
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