Instructions to use ProbeX/Model-J__ResNet__model_idx_0827 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_0827 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_0827") 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_0827") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0827") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a939142dc332a73fc6861f24383c36ead82baa220812525173fb75959db4542
- Size of remote file:
- 5.37 kB
- SHA256:
- d36c40aa898e3d62c1ab51845a9df779b8fca61281d55ed5ca488470c21b9822
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