Instructions to use ProbeX/Model-J__ResNet__model_idx_0666 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_0666 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_0666") 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_0666") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0666") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abd5e5bc5c109ecc2965e84b73ad044925afa7391d806a23df075835f3c4b533
- Size of remote file:
- 5.37 kB
- SHA256:
- b881bbe1a1239a901437f8f24e51f4c1a69ee12c6a3c980c3f2fe793ce24f6b1
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