Instructions to use ProbeX/Model-J__ResNet__model_idx_0664 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_0664 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_0664") 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_0664") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0664", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cefafb7563557d7047a25ea350fb2a28f12661bb843dc4982b098a4ff497c4a
- Size of remote file:
- 5.37 kB
- SHA256:
- d60b45afcf9d4868068fbca2a88f6c8a2220e0f0f5c433b70aa5a945856df15c
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