---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: banknote18k
  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. -->

# banknote18k

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: 0.0096
- Accuracy: 0.9987

## 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: 0.0002
- 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: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4947        | 0.12  | 100  | 0.3407          | 0.9451   |
| 0.423         | 0.23  | 200  | 0.2200          | 0.9451   |
| 0.2237        | 0.35  | 300  | 0.1613          | 0.9536   |
| 0.2806        | 0.46  | 400  | 0.0884          | 0.9810   |
| 0.1188        | 0.58  | 500  | 0.0512          | 0.9895   |
| 0.3279        | 0.7   | 600  | 0.0568          | 0.9876   |
| 0.1054        | 0.81  | 700  | 0.0342          | 0.9928   |
| 0.0924        | 0.93  | 800  | 0.0536          | 0.9863   |
| 0.1068        | 1.05  | 900  | 0.0746          | 0.9804   |
| 0.213         | 1.16  | 1000 | 0.0340          | 0.9948   |
| 0.159         | 1.28  | 1100 | 0.0426          | 0.9882   |
| 0.1048        | 1.39  | 1200 | 0.0248          | 0.9948   |
| 0.1493        | 1.51  | 1300 | 0.0154          | 0.9974   |
| 0.1274        | 1.63  | 1400 | 0.0394          | 0.9922   |
| 0.0915        | 1.74  | 1500 | 0.0422          | 0.9882   |
| 0.0598        | 1.86  | 1600 | 0.0219          | 0.9948   |
| 0.1241        | 1.97  | 1700 | 0.0173          | 0.9948   |
| 0.1249        | 2.09  | 1800 | 0.0179          | 0.9954   |
| 0.0131        | 2.21  | 1900 | 0.0124          | 0.9961   |
| 0.0392        | 2.32  | 2000 | 0.0123          | 0.9967   |
| 0.0655        | 2.44  | 2100 | 0.0223          | 0.9948   |
| 0.0355        | 2.56  | 2200 | 0.0256          | 0.9941   |
| 0.0335        | 2.67  | 2300 | 0.0147          | 0.9967   |
| 0.0618        | 2.79  | 2400 | 0.0123          | 0.9974   |
| 0.0476        | 2.9   | 2500 | 0.0110          | 0.9980   |
| 0.0452        | 3.02  | 2600 | 0.0192          | 0.9967   |
| 0.0104        | 3.14  | 2700 | 0.0184          | 0.9967   |
| 0.036         | 3.25  | 2800 | 0.0122          | 0.9974   |
| 0.0358        | 3.37  | 2900 | 0.0104          | 0.9987   |
| 0.054         | 3.48  | 3000 | 0.0101          | 0.9987   |
| 0.0395        | 3.6   | 3100 | 0.0132          | 0.9967   |
| 0.0367        | 3.72  | 3200 | 0.0096          | 0.9987   |
| 0.0261        | 3.83  | 3300 | 0.0101          | 0.9980   |
| 0.0017        | 3.95  | 3400 | 0.0096          | 0.9987   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3