Instructions to use ProbeX/Model-J__MAE__model_idx_0383 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_0383 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_0383") 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_0383") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0383", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf314c90fea87281077c280e7b2d8d49d3d043a2a24590430d4ebb28ebfbf130
- Size of remote file:
- 5.37 kB
- SHA256:
- 14a851029a617b09d20285fc369188d0bf6f2403d2a93e5dbfa4f963b2ce8a3d
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