Instructions to use ProbeX/Model-J__ResNet__model_idx_0257 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_0257 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_0257") 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_0257") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0257") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 846df9fa93b2633e1498068ce0168cc6a7fbeafdfc4f13ee5464e3816413b43d
- Size of remote file:
- 5.37 kB
- SHA256:
- d02e7bad1b0533e362ab7ea4b872de4802335bf9b449cd6d5a60bcd4e1a6b7be
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