---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-augmentation
  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-augmentation

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5668
- Accuracy: 0.8804
- Precision: 0.8823
- Recall: 0.8804
- F1: 0.8789

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 770
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9124        | 1.0   | 321  | 0.6025          | 0.7805   | 0.7788    | 0.7805 | 0.7683 |
| 0.5876        | 2.0   | 642  | 0.5819          | 0.7864   | 0.7990    | 0.7864 | 0.7820 |
| 0.5415        | 3.0   | 963  | 0.6149          | 0.8041   | 0.7943    | 0.8041 | 0.7865 |
| 0.4815        | 4.0   | 1284 | 0.4654          | 0.8294   | 0.8259    | 0.8294 | 0.8115 |
| 0.4263        | 5.0   | 1605 | 0.5481          | 0.8259   | 0.8315    | 0.8259 | 0.8023 |
| 0.3515        | 6.0   | 1926 | 0.4287          | 0.8592   | 0.8580    | 0.8592 | 0.8574 |
| 0.3144        | 7.0   | 2247 | 0.5005          | 0.8363   | 0.8320    | 0.8363 | 0.8270 |
| 0.2736        | 8.0   | 2568 | 0.5306          | 0.8294   | 0.8448    | 0.8294 | 0.8302 |
| 0.2519        | 9.0   | 2889 | 0.4733          | 0.8578   | 0.8534    | 0.8578 | 0.8534 |
| 0.2227        | 10.0  | 3210 | 0.4905          | 0.8585   | 0.8520    | 0.8585 | 0.8512 |
| 0.1724        | 11.0  | 3531 | 0.5050          | 0.8655   | 0.8671    | 0.8655 | 0.8628 |
| 0.1596        | 12.0  | 3852 | 0.5263          | 0.8686   | 0.8657    | 0.8686 | 0.8631 |
| 0.1397        | 13.0  | 4173 | 0.7043          | 0.8533   | 0.8703    | 0.8533 | 0.8488 |
| 0.1298        | 14.0  | 4494 | 0.6275          | 0.8679   | 0.8734    | 0.8679 | 0.8632 |
| 0.1029        | 15.0  | 4815 | 0.5564          | 0.8807   | 0.8776    | 0.8807 | 0.8772 |
| 0.0893        | 16.0  | 5136 | 0.5668          | 0.8804   | 0.8823    | 0.8804 | 0.8789 |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2