Instructions to use ProbeX/Model-J__MAE__model_idx_0901 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_0901 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_0901") 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_0901") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0901", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04c6cfb06ca018906577356c9525cd7b63fdcaa6d80ad6701f107497e8a2e8e1
- Size of remote file:
- 5.37 kB
- SHA256:
- 0ca2c205844125db2ded4b95755109b13a4923e2cb20787cb6d0dd3625416f45
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