Instructions to use ProbeX/Model-J__ResNet__model_idx_0366 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_0366 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_0366") 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_0366") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0366") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ecdd4c361519d85940ad5686dc2e6e3695a49cb3bcfa91de5bb26a626d699e87
- Size of remote file:
- 171 MB
- SHA256:
- 69312f4f70101330cfd7357d12e10f7c0e90147a85a0e569c78e3180f3f30648
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