Instructions to use ProbeX/Model-J__ResNet__model_idx_0328 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_0328 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_0328") 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_0328") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0328") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba2e95fca48648079e66eddd3ef551a7152784f467bb9d0813bdb499bec2c974
- Size of remote file:
- 5.37 kB
- SHA256:
- d8a84ca5aa44e83108b302060229e639e1830d2e50b2f5abbe5680a6c48d0a17
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