Instructions to use ProbeX/Model-J__MAE__model_idx_0185 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_0185 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_0185") 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_0185") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0185") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc0da034676a9bbed848546486284308e7b1d5ec3dfc99c6760f066793b05c32
- Size of remote file:
- 5.37 kB
- SHA256:
- 8baa1b9140e3a479f4efb304c7d93f7c4dd6f95ba77140dbfe03979ca3488937
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.