Instructions to use ProbeX/Model-J__ResNet__model_idx_0276 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_0276 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_0276") 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_0276") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0276") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf9692664ed17c03779677e93d7504df797001827ee9e06549ddcb5f7fe36839
- Size of remote file:
- 5.37 kB
- SHA256:
- fe358b576d17988dd33e1988379a541b684ae704ed6a5cb9e90d8fc8b466e348
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