Instructions to use ProbeX/Model-J__MAE__model_idx_0121 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_0121 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_0121") 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_0121") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0121", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c5a02894970845a4c3238e6398118d64dd2a4e62b58e7a902b69dcad5890d36
- Size of remote file:
- 5.37 kB
- SHA256:
- 960c181570d67d25086a8d9dc8fc483544f138e32c40e781183139681c2b3ed6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.