Instructions to use ProbeX/Model-J__ResNet__model_idx_0131 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_0131 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_0131") 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_0131") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0131") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 791318ff9f9d11d84cc797d9e1098b07fde64c56459bda6fd7a09ce5d7816a3a
- Size of remote file:
- 5.37 kB
- SHA256:
- e418be35241f57332cb97dbb203501e5aee00bab15fbc4e8ba1e37fe06898716
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