Instructions to use ProbeX/Model-J__ResNet__model_idx_0488 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_0488 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_0488") 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_0488") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0488") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8f8f4545af1ffdba1b43168df9f61bfb8d0c7bc4aa0016632710649e86a12d0
- Size of remote file:
- 5.37 kB
- SHA256:
- 7c0a6367523c989b9f6369bdcbb76deaf9fe8f423eee668d117285c69078099b
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