Instructions to use ProbeX/Model-J__ResNet__model_idx_0682 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_0682 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_0682") 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_0682") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0682") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f419faf5211233076ef12728180c3b0552fd16252f5232a9e738ba3748144fa
- Size of remote file:
- 5.37 kB
- SHA256:
- 4bb88ba3f1c7e9890cbd092004f22b28386e82952b88fc61fa383d731441c180
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