metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: vit-real-fake-cls
    results: []
datasets:
  - date3k2/raw_real_fake_images

ViT Real Fake Image Classification

This model is a fine-tuned version of google/vit-base-patch16-224 on Real & Fake Images dataset. It achieves the following results on the evaluation set:

Loss: 0.0398
Accuracy: 0.9866
F1: 0.9878
Recall: 0.9854
Precision: 0.9902

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 128
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.1759	1.0	59	0.2212	0.9173	0.9229	0.8978	0.9495
0.1903	2.0	118	0.1047	0.9629	0.9659	0.9503	0.9819
0.0463	3.0	177	0.0824	0.9699	0.9730	0.9834	0.9628
0.0015	4.0	236	0.0763	0.9764	0.9787	0.9825	0.9749
0.0631	5.0	295	0.0794	0.9737	0.9759	0.9640	0.9880
0.0114	6.0	354	0.0582	0.9801	0.9819	0.9786	0.9853
0.0004	7.0	413	0.0662	0.9807	0.9824	0.9796	0.9853
0.0231	8.0	472	0.0713	0.9753	0.9773	0.9659	0.9890
0.0017	9.0	531	0.0518	0.9817	0.9834	0.9796	0.9872
0.0268	10.0	590	0.0385	0.9839	0.9855	0.9903	0.9807

Framework versions

Transformers 4.41.0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1