Instructions to use ProbeX/Model-J__ResNet__model_idx_0735 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_0735 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_0735") 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_0735") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0735") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b6cbef6450c54e25a8d93858c8b6524e29dad4277903e584f01725d7566f2af
- Size of remote file:
- 5.37 kB
- SHA256:
- 9f4a8188084b80367f7747c320f6a5c7edcd06512a28d4ec8b0a407ee96ce8bf
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