Instructions to use ProbeX/Model-J__MAE__model_idx_0564 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_0564 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_0564") 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_0564") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0564", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8a0a0007100c69cd393cdb99e405f2cd9c2f8bb0869ae0ef0f5595d15fc357a
- Size of remote file:
- 5.37 kB
- SHA256:
- b0b7271f54ab89dc34b38d08380e86cefb96e7d79affe50569ebaa9a2bfc65e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.