Instructions to use ProbeX/Model-J__MAE__model_idx_0188 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__MAE__model_idx_0188 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__MAE__model_idx_0188") 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__MAE__model_idx_0188") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0188", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d93fb67d3909fa4d82bf0f3bc33a820c80ef57cbf5c190ccf5ba43fb96529c2
- Size of remote file:
- 5.37 kB
- SHA256:
- 0c76af0e2950a9fa34e5eaaff8b4d27297aabdc029839728ce6595d12f27dedf
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