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# FaceAIorNot

Face AI or Not

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0233
- Accuracy: 0.9935
- Precision: 0.9925
- Recall: 0.9947
- F1: 0.9936

## Model description

Two classes: AI-generated, Not AI-generated

## Intended uses & limitations

Classify an face image if is generated by AI.
The classify result may not is 100% right.

## Training and evaluation data

Finetune in 105,330 face images.
17 datasets.
14 AI Image Generation Techniques.
50% real faces and 50% AI-generated faces.
Data set cut into 90% Train set, 10% Test set(evaluation set).

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0862        | 1.0   | 740  | 0.0694          | 0.9740   | 0.9731    | 0.9756 | 0.9743 |
| 0.0914        | 2.0   | 1481 | 0.0396          | 0.9862   | 0.9814    | 0.9916 | 0.9865 |
| 0.0184        | 3.0   | 2222 | 0.0784          | 0.9777   | 0.9618    | 0.9955 | 0.9783 |
| 0.0181        | 4.0   | 2963 | 0.0330          | 0.9907   | 0.9879    | 0.9938 | 0.9908 |
| 0.03          | 4.99  | 3700 | 0.0233          | 0.9935   | 0.9925    | 0.9947 | 0.9936 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1

ai-generated not ai-generated