Instructions to use ProbeX/Model-J__MAE__model_idx_0235 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_0235 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_0235") 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_0235") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0235", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c2fe870ca4d7794f670a6621658718f2d55106b4e4f3e1b7b454bcf92c6e722
- Size of remote file:
- 5.37 kB
- SHA256:
- f260796e178d62472f9875c2fb837a5f3a19a9666f86134cfd9345dd6a95efc3
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