Instructions to use ProbeX/Model-J__MAE__model_idx_0188 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_0188 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_0188") 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_0188") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0188", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65359ff8927250026c1117514420faf9606f337556325dfc8dd9d4c584d6c5e3
- Size of remote file:
- 343 MB
- SHA256:
- 4533f0eda99e7874767c2501e304b559cb3368e032284bdab27aacd14f61cfed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.