Instructions to use ProbeX/Model-J__ResNet__model_idx_0019 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0019 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0019") 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("ProbeX/Model-J__ResNet__model_idx_0019") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0019") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b3c1e016113e1e74df275f0cb89d1979b53bbd13f8b5b294d4e808099fad568
- Size of remote file:
- 5.37 kB
- SHA256:
- ab8485b75c831f4ab900a21a63f8b3f10e28195a04f8eabecbe8cf14175c734f
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