Instructions to use ProbeX/Model-J__ResNet__model_idx_0737 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_0737 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_0737") 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_0737") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0737") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7442264d63d09ab3c9857c9d9134553e22a9b31d9b546d1b2469b107d336e2fa
- Size of remote file:
- 5.37 kB
- SHA256:
- 96df350ed7fa5569edb9301a6cf19f47e6f18bbfd4be911e339f28c9db8de4a5
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