Instructions to use ProbeX/Model-J__MAE__model_idx_0195 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_0195 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_0195") 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_0195") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0195") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94b715632945ab667b5c2d3ac7c1c5d59cec8b3c4cb344ba0ebc6ece8ad6e707
- Size of remote file:
- 5.37 kB
- SHA256:
- 89651d97cd884604427b0caa1f90b60e365bef8675e689751b34a290f62f2d1f
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