Instructions to use ProbeX/Model-J__ResNet__model_idx_0933 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_0933 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_0933") 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_0933") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0933") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f216da6746cea2f7e463ad925c040b96fc52234df1a5a6eb2b3b162124c9a78a
- Size of remote file:
- 5.37 kB
- SHA256:
- 78c5933d6dc157247525dc9044be0a8f2d56a95986b578dc62fb34b754764190
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