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:
- c7f391951bce6d93f3fa07d6c69e4e1f44a04bce7c39b668ba5af58bc9c261c7
- Size of remote file:
- 203 kB
- SHA256:
- f60f6678fa8662983937cec076f43efd218f2366ae9650c471586a915f092196
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