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---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: FaceAIorNot
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9935440994968195
- name: Precision
type: precision
value: 0.9925121677274429
- name: Recall
type: recall
value: 0.9947467166979362
- name: F1
type: f1
value: 0.9936281859070465
---
<!-- 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. -->
# 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
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