Instructions to use ProbeX/Model-J__ResNet__model_idx_0608 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_0608 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_0608") 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_0608") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0608") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 595eea2c56961996a7e69290fbe145fb90e6228762baa27ec7f9fbbb1ab1950c
- Size of remote file:
- 5.37 kB
- SHA256:
- 12f8b6330028dee7f30648b2207b525d6ee3387f309cd744abdea6eef0c3da77
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