Instructions to use ProbeX/Model-J__ResNet__model_idx_0439 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_0439 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_0439") 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_0439") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0439", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab738305e90ee0ed2231cb7b026143495e2dfa68162d6634b56360d72c23107b
- Size of remote file:
- 171 MB
- SHA256:
- 028f13825fa1f672f7a4eeabbf377a4b1fd1204a6d6bb116c7ee35c0d68b0e37
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