---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
widget:
- src: https://huggingface.co/Alex14005/model-Dementia-classification-Alejandro-Arroyo/raw/main/Mild-demented.jpg
  example_title: Mild Demented
- src: https://huggingface.co/Alex14005/model-Dementia-classification-Alejandro-Arroyo/raw/main/No-demented.jpg
  example_tittle: No Demented
model-index:
- name: model-Dementia-classification-Alejandro-Arroyo
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: RiniPL/Dementia_Dataset
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9230769230769231
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# model-Dementia-classification-Alejandro-Arroyo

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the RiniPL/Dementia_Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1858
- Accuracy: 0.9231

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results



### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3