Instructions to use ProbeX/Model-J__ResNet__model_idx_0822 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_0822 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_0822") 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_0822") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0822", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9bb626f451f01daf037ebfc874d380b6848922ec852b776bd61ad4f73e2683d
- Size of remote file:
- 171 MB
- SHA256:
- 2592371962334d4505ab3c9e74f3e6f4afabe4ceb04614d4a197d3f543ee867d
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