Instructions to use ProbeX/Model-J__ResNet__model_idx_0879 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_0879 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_0879") 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_0879") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0879") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b659d6fa6ff84f7dbf032cd4da5112eb8d79900b14a732e39168327db4784fba
- Size of remote file:
- 5.37 kB
- SHA256:
- 2883ffc382b317249858b7aca204987ed2a9f7c8dc40e4cbbcd3d88f22964022
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