Instructions to use ProbeX/Model-J__MAE__model_idx_0876 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_0876 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_0876") 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_0876") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0876") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e54aaf87269733b024d9f7c886baf940fc7b2bd776114b4ec90a00dc5a9a953
- Size of remote file:
- 5.37 kB
- SHA256:
- 5a33da94f60efb765a286d8d33ce0cb562a823fd74d9dce78c60752ae6db0930
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.