---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- fair_face
metrics:
- accuracy
model-index:
- name: vit-base-age-classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: fair_face
      type: fair_face
      config: '0.25'
      split: train
      args: '0.25'
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.987904862407663
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-age-classification

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the fair_face dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0743
- Accuracy: 0.9879

## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2011        | 1.0   | 385  | 1.0297          | 0.5664   |
| 0.8578        | 2.0   | 770  | 0.7667          | 0.6936   |
| 0.5961        | 3.0   | 1155 | 0.4088          | 0.8703   |
| 0.3073        | 4.0   | 1540 | 0.1689          | 0.9581   |
| 0.1146        | 5.0   | 1925 | 0.0743          | 0.9879   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1