Instructions to use ProbeX/Model-J__ResNet__model_idx_0909 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_0909 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_0909") 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_0909") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0909", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5740bd4174f9da9361e13d632768998917cdd3645875ec31bb1884b8001459b7
- Size of remote file:
- 171 MB
- SHA256:
- ffb1c7f4e4f3612ffb4a8f0becbaf24ac43f7c066b07826a9c5940e822fdf3ca
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