Instructions to use ProbeX/Model-J__ResNet__model_idx_0798 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_0798 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_0798") 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_0798") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0798") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25448dcf826685e75d3fa2ab191726c1013ef0d4e453b9a827f41079d00c412e
- Size of remote file:
- 5.37 kB
- SHA256:
- d2f397b89ecf905d55e1fcb993d0c0e1ccda8c2982db921e1a169624a13889b1
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