Instructions to use ProbeX/Model-J__ResNet__model_idx_0679 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_0679 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_0679") 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_0679") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0679") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 069978460eb96aa09c286955d88e5240c213d95d87a2ae8cb543c54c99cfe88e
- Size of remote file:
- 5.37 kB
- SHA256:
- 29490201d9aba62ee10b630b0a394ac32ebe1fe47067050c348fa76d9b23b977
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