Instructions to use ProbeX/Model-J__MAE__model_idx_0213 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_0213 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_0213") 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_0213") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0213") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d342470d88e21fa08681e68614552016602aa4120b4196e650e058ae21d5b9c
- Size of remote file:
- 5.37 kB
- SHA256:
- 89195e5c44cd01e1d99fad05d2b4189560fa77342e22fb6da9e18e3b95dfc8a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.