---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- fair_face
metrics:
- accuracy
model-index:
- name: initial_ViT_model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: fair_face
      type: fair_face
      config: '0.25'
      split: validation
      args: '0.25'
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.21252510498448055
---

<!-- 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. -->

# initial_ViT_model

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: 3.6347
- Accuracy: 0.2125

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.7855        | 0.15  | 50   | 4.6444          | 0.0511   |
| 4.4242        | 0.29  | 100  | 4.2124          | 0.1418   |
| 4.0596        | 0.44  | 150  | 3.9402          | 0.1744   |
| 3.859         | 0.59  | 200  | 3.7823          | 0.1956   |
| 3.7392        | 0.74  | 250  | 3.6877          | 0.2105   |
| 3.6424        | 0.88  | 300  | 3.6347          | 0.2125   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0