Instructions to use dewifaj/alzheimer_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dewifaj/alzheimer_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dewifaj/alzheimer_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dewifaj/alzheimer_classification") model = AutoModelForImageClassification.from_pretrained("dewifaj/alzheimer_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dewifaj/alzheimer_classification: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/dewifaj/alzheimer_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://dewifaj/alzheimer_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dewifaj/alzheimer_classification/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 86989dc3e8ca47e03f6d9f51ae9a24201bca3c5fb8f25a07df3e5c77bac185d4
- Size of remote file:
- 5.11 kB
- SHA256:
- 66653f36e83b78048c8482748d5394708fc0830419b0b1f608f3e47e13ef0857
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.