Instructions to use ProbeX/Model-J__ResNet__model_idx_0906 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_0906 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_0906") 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_0906") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0906") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6de719f5bb94aadf6cea0f35625d6eb9de46559da9c07fba89d82fef6e5da90a
- Size of remote file:
- 5.37 kB
- SHA256:
- 11d127ee89732528bbe7d1313ed7eba5c5a5affbe44d5aad8ca96acd63001f01
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