Instructions to use ProbeX/Model-J__MAE__model_idx_0535 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_0535 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_0535") 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_0535") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0535", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a20cab88d35e5aeb2bd9f58acf6e1a7efb967eb1d069ef3ca004ce952d6b9aab
- Size of remote file:
- 5.37 kB
- SHA256:
- 3a1e58ce7dc434139456ddac49e14b46096e8dfba8d30337dd1caf1a4b4bcf78
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