---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: realFake-img
  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. -->

# realFake-img

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: 0.0988
- Accuracy: 0.9785

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2578        | 0.2525 | 100  | 0.1594          | 0.9418   |
| 0.0944        | 0.5051 | 200  | 0.2243          | 0.9373   |
| 0.1747        | 0.7576 | 300  | 0.2472          | 0.9293   |
| 0.1328        | 1.0101 | 400  | 0.1774          | 0.9338   |
| 0.1918        | 1.2626 | 500  | 0.1282          | 0.9570   |
| 0.169         | 1.5152 | 600  | 0.2247          | 0.9346   |
| 0.2595        | 1.7677 | 700  | 0.1785          | 0.9445   |
| 0.0911        | 2.0202 | 800  | 0.1353          | 0.9534   |
| 0.0548        | 2.2727 | 900  | 0.1998          | 0.9472   |
| 0.1399        | 2.5253 | 1000 | 0.1971          | 0.9445   |
| 0.2001        | 2.7778 | 1100 | 0.2479          | 0.9373   |
| 0.0976        | 3.0303 | 1200 | 0.1601          | 0.9499   |
| 0.1291        | 3.2828 | 1300 | 0.1607          | 0.9588   |
| 0.0721        | 3.5354 | 1400 | 0.1822          | 0.9588   |
| 0.0592        | 3.7879 | 1500 | 0.1255          | 0.9624   |
| 0.0964        | 4.0404 | 1600 | 0.1620          | 0.9543   |
| 0.0738        | 4.2929 | 1700 | 0.1279          | 0.9651   |
| 0.0504        | 4.5455 | 1800 | 0.1624          | 0.9588   |
| 0.0972        | 4.7980 | 1900 | 0.1579          | 0.9624   |
| 0.0456        | 5.0505 | 2000 | 0.1965          | 0.9490   |
| 0.0334        | 5.3030 | 2100 | 0.1652          | 0.9570   |
| 0.0242        | 5.5556 | 2200 | 0.1182          | 0.9749   |
| 0.0715        | 5.8081 | 2300 | 0.1250          | 0.9651   |
| 0.0407        | 6.0606 | 2400 | 0.1172          | 0.9696   |
| 0.0003        | 6.3131 | 2500 | 0.0819          | 0.9785   |
| 0.0072        | 6.5657 | 2600 | 0.1406          | 0.9714   |
| 0.0183        | 6.8182 | 2700 | 0.1152          | 0.9749   |
| 0.0021        | 7.0707 | 2800 | 0.1368          | 0.9731   |
| 0.046         | 7.3232 | 2900 | 0.0900          | 0.9794   |
| 0.033         | 7.5758 | 3000 | 0.1014          | 0.9785   |
| 0.0354        | 7.8283 | 3100 | 0.0968          | 0.9767   |
| 0.0026        | 8.0808 | 3200 | 0.1217          | 0.9731   |
| 0.0002        | 8.3333 | 3300 | 0.0828          | 0.9794   |
| 0.0006        | 8.5859 | 3400 | 0.0926          | 0.9794   |
| 0.0006        | 8.8384 | 3500 | 0.1001          | 0.9794   |
| 0.0006        | 9.0909 | 3600 | 0.0863          | 0.9848   |
| 0.0633        | 9.3434 | 3700 | 0.0911          | 0.9803   |
| 0.0009        | 9.5960 | 3800 | 0.0941          | 0.9821   |
| 0.0247        | 9.8485 | 3900 | 0.0988          | 0.9785   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1