Instructions to use ProbeX/Model-J__MAE__model_idx_0180 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_0180 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_0180") 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_0180") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0180", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fab401e820b7958eaa87026f53468ab4e8ae5ebc578597cf3f90237c18911f58
- Size of remote file:
- 5.37 kB
- SHA256:
- 4ad018ecf86d3dbbba0d6f4f40e0a059d7465ef6ed1190ded64ef229386ab2f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.