Instructions to use ProbeX/Model-J__ResNet__model_idx_0978 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_0978 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_0978") 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_0978") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0978") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 706d6b5792598ade65dd819874f70f8bf507be490658fbe99f3e5b031c77999f
- Size of remote file:
- 5.37 kB
- SHA256:
- 602b17c766cd8dbc6a8a8acad59edfb43352f2fc3b1a32332657d15278804916
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