Instructions to use ProbeX/Model-J__ResNet__model_idx_0872 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_0872 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_0872") 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_0872") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0872") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85c00fddea47bc639132106220f1c4dc508e1f7391573b4e6474d244997e7035
- Size of remote file:
- 5.37 kB
- SHA256:
- 4c81778b398e55c84a037593b1435ebd3f75fe8ee4d11acbae388a4cf58c85d7
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