Instructions to use ProbeX/Model-J__ResNet__model_idx_0549 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_0549 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_0549") 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_0549") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0549") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29a80abb542c8430c584c3ab4822c0d71f8a508a46a7f96cb8d7ccf054bbf6d3
- Size of remote file:
- 5.37 kB
- SHA256:
- 58476c8165252ec0f8477b74de3f68820bdb91714e9ef19647109fe2d2399f0a
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