Instructions to use ProbeX/Model-J__ResNet__model_idx_0541 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_0541 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_0541") 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_0541") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0541") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f3309e9ccb4b727c354c365d579c04d2b7e9986978aac313d71982d00bcc5f37
- Size of remote file:
- 5.37 kB
- SHA256:
- 735ba66508e6fa935e97e2dc8fb2dba94dcc2804b56e66d956993109be399132
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