Instructions to use hf-tiny-model-private/tiny-random-ResNetModel 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-ResNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-ResNetModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ResNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ResNetModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df79b03adcdcd2bd626c949b1defca893f5c46938815aeb031505ebf05c41025
- Size of remote file:
- 83.4 kB
- SHA256:
- dbef06c95d54af8c2370e3cf7d93229e2467e6a6992e92495862bb03f957a01f
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