Instructions to use ProbeX/Model-J__ResNet__model_idx_0800 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_0800 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_0800") 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_0800") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0800") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09e02b22099c473ecf03928d55b5deca33c3ea2975950f61bdfade4100ed5d02
- Size of remote file:
- 5.37 kB
- SHA256:
- 414f2a400b6817bd221f6814261fa63a240eca583ab7478c06d54cf3569e8a3d
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