Instructions to use ProbeX/Model-J__ResNet__model_idx_0715 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_0715 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_0715") 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_0715") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0715") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6789165b57d078a818e472e99b8115d144df0956ee8ec0c84e8431cc5f669633
- Size of remote file:
- 5.37 kB
- SHA256:
- 9421a7db4ae091acaccf51a279945c7f0c9a6282251a0998bbba6c488d00c0cc
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