Instructions to use ProbeX/Model-J__ResNet__model_idx_0594 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_0594 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_0594") 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_0594") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0594") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 755a12a9617ad183567e95ba8262661f122778183ba56f7261e99c9f73a6f441
- Size of remote file:
- 5.37 kB
- SHA256:
- 30046b08ce4f29432fec02a11aaec51688e7a8f5ec508c18774d1fb063d4371d
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