Instructions to use ProbeX/Model-J__ResNet__model_idx_0323 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_0323 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_0323") 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_0323") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0323") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eceee16c6cfb52d8315df8790e02acf67605358e5ee75b4b61874ab703dbbd87
- Size of remote file:
- 5.37 kB
- SHA256:
- df796fbfa8309d2934eae103838e0c57c5839c0caee01b195a297b398e61a977
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