Instructions to use ProbeX/Model-J__ResNet__model_idx_0148 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_0148 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_0148") 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_0148") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0148") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32befe15a4a562ddfd208bb442e80f0ecf58779f51da25aee292d9df9876b67d
- Size of remote file:
- 5.37 kB
- SHA256:
- ea731c4cba7108af03664b32f8e0e69dbc4ed38b693430b567ef7daf08ae64cb
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