Instructions to use ProbeX/Model-J__ResNet__model_idx_0575 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_0575 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_0575") 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_0575") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0575") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea8e18c318243a91c3b35129a5c2dd2de824cad3c2a0b588a963a29d03b51a6f
- Size of remote file:
- 5.37 kB
- SHA256:
- 415c4e03049bcb094a66ff7111029b3db3fc1c1ded4f1d107530c52b28ed1a65
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