Instructions to use ProbeX/Model-J__ResNet__model_idx_0521 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_0521 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_0521") 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_0521") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0521") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0881af1c272c3d7413fc0a0b13a56c7437dc784162ccf128cfd05253b0fc197
- Size of remote file:
- 171 MB
- SHA256:
- ad473ac7454ca9d6b157fbd0326c01558c9acbfada8a09fc7b3dcf65d281851c
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