Instructions to use ProbeX/Model-J__ResNet__model_idx_0069 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_0069 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_0069") 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_0069") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0069") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4def0da5fc49c39a937307439c74294a708f72eca602ec84f42101387a433006
- Size of remote file:
- 5.37 kB
- SHA256:
- 71298ce4f1c6c1226cbfa13ee5ebf6d923de4e5b26972479eb196d1f4de24278
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