Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Falah/Alzheimer_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falah/Alzheimer_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Falah/Alzheimer_classification_model") 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("Falah/Alzheimer_classification_model") model = AutoModelForImageClassification.from_pretrained("Falah/Alzheimer_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69e7c17cf355dd2a761c00f5ba878e4a481d124fc24802211609eb3c09ef3ae1
- Size of remote file:
- 343 MB
- SHA256:
- cab12dddb072463d30fdb8d9c2809853add8c588f5a4029b1cb1b08e266e52fb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.