Instructions to use ProbeX/Model-J__ResNet__model_idx_0853 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_0853 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_0853") 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_0853") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0853", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe9a75155e9df64c7ac8f3b6b41752c9b76f972c19124d749969f6a9f5c82757
- Size of remote file:
- 171 MB
- SHA256:
- 28f8e8eb227ecf338cff3e7b2b5735e491160038b2ec8fd432ff71f1830333bc
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