---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: google-vit-base-patch16-224-face
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7248574809078198
    - name: Precision
      type: precision
      value: 0.717172031675939
    - name: Recall
      type: recall
      value: 0.7248574809078198
    - name: F1
      type: f1
      value: 0.7195690317790054
---

<!-- 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. -->

# google-vit-base-patch16-224-face

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4531
- Accuracy: 0.7249
- Precision: 0.7172
- Recall: 0.7249
- F1: 0.7196

## 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: 0.00012
- 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: 0.1
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8514        | 1.0   | 290  | 0.8464          | 0.7048   | 0.7035    | 0.7048 | 0.6909 |
| 0.7202        | 2.0   | 580  | 0.7791          | 0.7283   | 0.7297    | 0.7283 | 0.7111 |
| 0.5455        | 3.0   | 870  | 0.7950          | 0.7285   | 0.7174    | 0.7285 | 0.7171 |
| 0.334         | 4.0   | 1160 | 0.8948          | 0.7155   | 0.7152    | 0.7155 | 0.7145 |
| 0.1644        | 5.0   | 1450 | 1.0820          | 0.7239   | 0.7189    | 0.7239 | 0.7194 |
| 0.0482        | 6.0   | 1740 | 1.2792          | 0.7204   | 0.7144    | 0.7204 | 0.7160 |
| 0.0236        | 7.0   | 2030 | 1.4162          | 0.7279   | 0.7195    | 0.7279 | 0.7209 |
| 0.0049        | 8.0   | 2320 | 1.4531          | 0.7249   | 0.7172    | 0.7249 | 0.7196 |


### Framework versions

- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1