Instructions to use ProbeX/Model-J__MAE__model_idx_0105 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_0105 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_0105") 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_0105") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0105", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6345bff50e72ce1c164468f33104c47e57362fd5d06fc5c75cc854e646c3b877
- Size of remote file:
- 5.37 kB
- SHA256:
- 92334c18256758b6c98231cb89a82ab8577d04d9c301d454941f534a4c82959f
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