---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-face-recognition
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.999957997311828
      name: Accuracy
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: custom
      type: custom
      split: test
    metrics:
    - type: precision
      value: 1.0
      name: Precision
    - type: roc_auc
      value: 0.908055
      name: AUC
    - type: recall
      value: 1.0
      name: Recall
---

<!-- 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-patch16-224-in21k-face-recognition

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0015
- Accuracy: 1.0000

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0368        | 1.0   | 372  | 0.0346          | 1.0000   |
| 0.0094        | 2.0   | 744  | 0.0092          | 1.0000   |
| 0.0046        | 3.0   | 1116 | 0.0047          | 1.0000   |
| 0.0029        | 4.0   | 1488 | 0.0029          | 1.0      |
| 0.0022        | 5.0   | 1860 | 0.0023          | 0.9999   |
| 0.0017        | 6.0   | 2232 | 0.0017          | 1.0      |
| 0.0015        | 7.0   | 2604 | 0.0015          | 1.0      |
| 0.0014        | 8.0   | 2976 | 0.0015          | 1.0000   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.11.0