Instructions to use ProbeX/Model-J__ResNet__model_idx_0920 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_0920 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_0920") 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_0920") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0920") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c77812bcd11f026416d794a4b40ec20a83ed98681a59213483dc9141b83814a
- Size of remote file:
- 5.37 kB
- SHA256:
- a2d95a87a8ffd48e663d884bee560f491981b7c7de12130a9c72c4f0e96c6924
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