Instructions to use ProbeX/Model-J__ResNet__model_idx_0325 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_0325 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_0325") 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_0325") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0325", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c249a4ddc937e8928bd61551ca20c65bfed145cfd5fa69ac2931c653b820b3a
- Size of remote file:
- 171 MB
- SHA256:
- 8696d50d7266d5b8a70e6093a48afefdd4bde6bf31ce80ec7088b0b4e4581b0e
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