Instructions to use ProbeX/Model-J__ResNet__model_idx_0698 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_0698 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_0698") 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_0698") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0698") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba6adcfa5792330d65acc4b928a6f916f2c4c8d49833a313dd1c7cd96f084cff
- Size of remote file:
- 5.37 kB
- SHA256:
- 9cd346f96a48381291e7cc7d14b25644e09dc7974709847972c1f5d3ecc432a4
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