Instructions to use ProbeX/Model-J__MAE__model_idx_0787 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_0787 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_0787") 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_0787") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0787") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ddde2ae63c5d4790c7ae855bc0aa938eeba8c4cf9298af099d00b7a41336f84
- Size of remote file:
- 5.37 kB
- SHA256:
- 3d7643a7b94324b6aac017b5af1a5b57813996a92db2b372535df253a09a12cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.