Instructions to use ProbeX/Model-J__ResNet__model_idx_0103 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_0103 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_0103") 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_0103") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0103") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe459059bfcc83b8ce42eaf70f8a3550646df845f2183671f411e0cc9aec960d
- Size of remote file:
- 5.37 kB
- SHA256:
- 820291488ed777358aca45d7b21606d05576262ad63a947b38ab28ea55bb82c8
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