Instructions to use ProbeX/Model-J__MAE__model_idx_0810 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_0810 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_0810") 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_0810") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0810") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bcb2e73d4ff7f95b5170b43973bd888b17448f3d4f46c15071ccaed7c88ebfad
- Size of remote file:
- 5.37 kB
- SHA256:
- c9d0f7f929f9b5f6cb27768138c9fb775c4aa5d9df4d39e40fa16ba9a3d2cc7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.