Instructions to use ProbeX/Model-J__ResNet__model_idx_0566 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_0566 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_0566") 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_0566") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0566") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6072b112b8b15cb3dd98174b611109fcd8ed4e57984eddba37b9e1580b5c7d24
- Size of remote file:
- 5.37 kB
- SHA256:
- 3edabe93c3e3f59a0716f2ab103b4519ac18ef3f03d78525d4ecf9f8795adeaf
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