Instructions to use ProbeX/Model-J__ResNet__model_idx_0830 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_0830 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_0830") 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_0830") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0830") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f6cf26e92f3ba6e3e4669014dccb1f608f3a24e9863016b596d6c0b86e65196
- Size of remote file:
- 5.37 kB
- SHA256:
- 223a866cb52d9da5ed8c80ebafeb93e0a30c2a8b03db3e6f055f3cecccb076c4
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