README.md

metadata

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

FaceAIorNot

Face AI or Not

This model is a fine-tuned version of 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