Instructions to use ProbeX/Model-J__ResNet__model_idx_0396 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_0396 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_0396") 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_0396") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0396") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb598d4617ba22b9cc9d038fd23da10dd431ae62a9e6e1c3d2a43cbff1bcb334
- Size of remote file:
- 5.37 kB
- SHA256:
- c789f4296584297751cc64ff8aafac9a951617bdb18842286f53d7c6c589c762
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