---
license: apache-2.0
base_model: dima806/deepfake_vs_real_image_detection
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: realFake-food
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: ai_real_images
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8013698630136986
---

<!-- 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-food

This model is a fine-tuned version of [dima806/deepfake_vs_real_image_detection](https://huggingface.co./dima806/deepfake_vs_real_image_detection) on the ai_real_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4344
- Accuracy: 0.8014

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3941        | 1.9231 | 100  | 0.4344          | 0.8014   |
| 0.2366        | 3.8462 | 200  | 0.4853          | 0.8630   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1