VIT_AI_image_detector
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0295
- Accuracy: 0.9924
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|---|---|---|---|---|
| 0.1686 | 1.0 | 1093 | 0.0843 | 0.9697 |
| 0.1195 | 2.0 | 2187 | 0.0731 | 0.9728 |
| 0.072 | 3.0 | 3281 | 0.0543 | 0.9803 |
| 0.1072 | 4.0 | 4375 | 0.0348 | 0.9884 |
| 0.079 | 5.0 | 5468 | 0.0342 | 0.9886 |
| 0.0681 | 6.0 | 6562 | 0.0317 | 0.9903 |
| 0.0513 | 7.0 | 7656 | 0.0304 | 0.9914 |
| 0.0518 | 8.0 | 8750 | 0.0293 | 0.9916 |
| 0.0674 | 9.0 | 9843 | 0.0295 | 0.9924 |
| 0.058 | 9.99 | 10930 | 0.0313 | 0.9917 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
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