Instructions to use ProbeX/Model-J__ResNet__model_idx_0787 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_0787 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_0787") 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_0787") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0787", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ccd1a6089f8d0275795dbf340e6325549d27bf25ea58746053ee8e3cb7f4544a
- Size of remote file:
- 5.37 kB
- SHA256:
- 2ed8ddd281edcc6dd6ac35aee5ac2ae4bc8de2d3f784047933bc697361d2daa3
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