---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- fakevsreal
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: real_vs_fake_image_model_vit_base
  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. -->

# real_vs_fake_image_model_vit_base

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0189
- Accuracy: 0.9953

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0094        | 0.1883 | 100  | 0.0243          | 0.9941   |
| 0.0165        | 0.3766 | 200  | 0.0351          | 0.9901   |
| 0.0239        | 0.5650 | 300  | 0.0470          | 0.9876   |
| 0.0179        | 0.7533 | 400  | 0.0678          | 0.9856   |
| 0.0166        | 0.9416 | 500  | 0.0296          | 0.9920   |
| 0.0138        | 1.1299 | 600  | 0.0337          | 0.9926   |
| 0.0574        | 1.3183 | 700  | 0.1020          | 0.9772   |
| 0.0256        | 1.5066 | 800  | 0.0612          | 0.9847   |
| 0.0327        | 1.6949 | 900  | 0.0616          | 0.9846   |
| 0.0086        | 1.8832 | 1000 | 0.0272          | 0.9923   |
| 0.008         | 2.0716 | 1100 | 0.0329          | 0.9920   |
| 0.0014        | 2.2599 | 1200 | 0.0250          | 0.9939   |
| 0.0132        | 2.4482 | 1300 | 0.0248          | 0.9937   |
| 0.0189        | 2.6365 | 1400 | 0.0266          | 0.9936   |
| 0.0034        | 2.8249 | 1500 | 0.0225          | 0.9948   |
| 0.009         | 3.0132 | 1600 | 0.0240          | 0.9942   |
| 0.0009        | 3.2015 | 1700 | 0.0244          | 0.9942   |
| 0.0054        | 3.3898 | 1800 | 0.0339          | 0.9928   |
| 0.0046        | 3.5782 | 1900 | 0.0248          | 0.9945   |
| 0.0135        | 3.7665 | 2000 | 0.0245          | 0.9945   |
| 0.0274        | 3.9548 | 2100 | 0.0241          | 0.9947   |
| 0.0031        | 4.1431 | 2200 | 0.0225          | 0.9947   |
| 0.0121        | 4.3315 | 2300 | 0.0210          | 0.9952   |
| 0.0055        | 4.5198 | 2400 | 0.0209          | 0.9953   |
| 0.0183        | 4.7081 | 2500 | 0.0197          | 0.9955   |
| 0.0077        | 4.8964 | 2600 | 0.0189          | 0.9953   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0