Instructions to use ProbeX/Model-J__ResNet__model_idx_0127 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_0127 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_0127") 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_0127") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0127") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 191e108beaf3dde7a8db3db79ba1adf0dbf61fcddce8df001ecfeb6222f16551
- Size of remote file:
- 5.37 kB
- SHA256:
- 159dea074e83a5c1bf59d3444b29f552f601406685f460003268ebbba204221f
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