Instructions to use ProbeX/Model-J__ResNet__model_idx_0781 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_0781 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_0781") 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_0781") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0781") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52611c5047537b615b46c8f0980ebaa5919f6549735e677a53615b5767327f79
- Size of remote file:
- 5.37 kB
- SHA256:
- 02d238a1088d136563a5fc1b8f6da7b42e504fd298a943677b2e4f31f8925104
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