Instructions to use ProbeX/Model-J__ResNet__model_idx_0113 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_0113 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_0113") 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_0113") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0113") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 411e9d7fc1eaa3403fb71a6dcd42f01d817cd13ae8a65ef3397ad7bcfe2f867a
- Size of remote file:
- 5.37 kB
- SHA256:
- 1f0f4c2535bdbcd08df118b439b56ca54fb0275de02f51646a653764acadec6f
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