Instructions to use ProbeX/Model-J__ResNet__model_idx_0775 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_0775 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_0775") 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_0775") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0775") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c715b7ee4e9a5d8efb9682a60f2109476eae060ffd0d76a95c12ca72e1d801d1
- Size of remote file:
- 5.37 kB
- SHA256:
- bc25d9d78954069b2a59ea9c06d81b88dda24ddc7a5de025c2d712eba9107a67
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