Instructions to use ProbeX/Model-J__ResNet__model_idx_0922 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_0922 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_0922") 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_0922") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0922") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0265a1a53c6aca4d4466b7683b99f8e5bcca5b8bb2b904c0794c5b56fc8f88ab
- Size of remote file:
- 5.37 kB
- SHA256:
- efa055713c68727bd13f001418fc4b5677d6027cef7bcc43121bdf20204005f2
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