Instructions to use ProbeX/Model-J__ResNet__model_idx_0694 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_0694 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_0694") 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_0694") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0694") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b22e90d61ec5bf2123e1b5060bdb0cf5fa60ea486e751c1ac751c52abafbd4a
- Size of remote file:
- 5.37 kB
- SHA256:
- 9b00f06f6c0c0835c7cad4dab4aa9b0318c61c99868091bf6106505f04c981b5
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