Instructions to use ProbeX/Model-J__ResNet__model_idx_0408 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_0408 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_0408") 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_0408") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0408") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59b28020152f5e2a92eb1fd2fc597f04bb9f9f063c081b655659a088ee722bd2
- Size of remote file:
- 5.37 kB
- SHA256:
- 04a3701e94faf04cf73e58b6cc7304fcdd3fb16ecf893d792f10b66889fe680d
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