Instructions to use ProbeX/Model-J__ResNet__model_idx_0264 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_0264 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_0264") 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_0264") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0264") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 37b28efac7b09f840bd9869aba23d9e464c3cfdf9102024b23565cb34e2332ad
- Size of remote file:
- 5.37 kB
- SHA256:
- c6ed12c567d063e8380e0adefba521d5fecf1d71a7d0f4b92dbd4ede6588cd9d
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