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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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