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vit-real-fake-classification-v2

Image Classification
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vit
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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

Visualize in Weights & Biases

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