---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ViT-VGGFace
  results: []
---

<!-- 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-VGGFace

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8361
- Accuracy: 0.8306

## 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: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6402        | 0.9982 | 416  | 1.7366          | 0.7127   |
| 1.1955        | 1.9988 | 833  | 1.2342          | 0.7782   |
| 0.9051        | 2.9994 | 1250 | 1.0314          | 0.8023   |
| 0.7446        | 4.0    | 1667 | 0.9074          | 0.8172   |
| 0.8081        | 4.9910 | 2080 | 0.8361          | 0.8306   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+rocm6.0
- Datasets 2.20.0
- Tokenizers 0.19.1