Instructions to use ProbeX/Model-J__ResNet__model_idx_0950 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_0950 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_0950") 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_0950") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0950") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b162fc9fd7e117fb55c5f7f5baca68a3c2855daf89a713dbf807a61eca18e52b
- Size of remote file:
- 5.37 kB
- SHA256:
- b59bf9bfa767e3556255580e0d439496039c14dde30b44b45ee58756abfa8522
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