Instructions to use ProbeX/Model-J__ResNet__model_idx_0075 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_0075 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_0075") 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_0075") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0075") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e387059f6ae0d5517498e3609d18a3f6d69815312f326f7ac1a0aa2ddb9ff7bd
- Size of remote file:
- 5.37 kB
- SHA256:
- c597bb9981c6b2d57832ca2ecd43730355f90a642a09e73476814fe7ba89b00f
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