Instructions to use ProbeX/Model-J__MAE__model_idx_0590 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_0590 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_0590") 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_0590") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0590", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d16d7f5c4d3fb5807c925a46c4afa0c60f718251522b8a55224af3c7229711f
- Size of remote file:
- 5.37 kB
- SHA256:
- b2fa5b9ce09f1d4a975013b35ba5728b8d1bf9326aeeba4837af2a5779a4eb42
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