Instructions to use ProbeX/Model-J__ResNet__model_idx_0980 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_0980 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_0980") 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_0980") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0980", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a6e98853daf4b0d76db4633a03ff1e5d13f9c3309982889af3d63cb75be7e15
- Size of remote file:
- 171 MB
- SHA256:
- 4c8a50d424dae748b6f2f462d01571e485f6e4a1c7695f8f78a3590f4bf123ed
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