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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: realFake-img
results: []
---
<!-- 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. -->
# realFake-img
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the ai_real_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0819
- Accuracy: 0.9785
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2578 | 0.2525 | 100 | 0.1594 | 0.9418 |
| 0.0944 | 0.5051 | 200 | 0.2243 | 0.9373 |
| 0.1747 | 0.7576 | 300 | 0.2472 | 0.9293 |
| 0.1328 | 1.0101 | 400 | 0.1774 | 0.9338 |
| 0.1918 | 1.2626 | 500 | 0.1282 | 0.9570 |
| 0.169 | 1.5152 | 600 | 0.2247 | 0.9346 |
| 0.2595 | 1.7677 | 700 | 0.1785 | 0.9445 |
| 0.0911 | 2.0202 | 800 | 0.1353 | 0.9534 |
| 0.0548 | 2.2727 | 900 | 0.1998 | 0.9472 |
| 0.1399 | 2.5253 | 1000 | 0.1971 | 0.9445 |
| 0.2001 | 2.7778 | 1100 | 0.2479 | 0.9373 |
| 0.0976 | 3.0303 | 1200 | 0.1601 | 0.9499 |
| 0.1291 | 3.2828 | 1300 | 0.1607 | 0.9588 |
| 0.0721 | 3.5354 | 1400 | 0.1822 | 0.9588 |
| 0.0592 | 3.7879 | 1500 | 0.1255 | 0.9624 |
| 0.0964 | 4.0404 | 1600 | 0.1620 | 0.9543 |
| 0.0738 | 4.2929 | 1700 | 0.1279 | 0.9651 |
| 0.0504 | 4.5455 | 1800 | 0.1624 | 0.9588 |
| 0.0972 | 4.7980 | 1900 | 0.1579 | 0.9624 |
| 0.0456 | 5.0505 | 2000 | 0.1965 | 0.9490 |
| 0.0334 | 5.3030 | 2100 | 0.1652 | 0.9570 |
| 0.0242 | 5.5556 | 2200 | 0.1182 | 0.9749 |
| 0.0715 | 5.8081 | 2300 | 0.1250 | 0.9651 |
| 0.0407 | 6.0606 | 2400 | 0.1172 | 0.9696 |
| 0.0003 | 6.3131 | 2500 | 0.0819 | 0.9785 |
| 0.0072 | 6.5657 | 2600 | 0.1406 | 0.9714 |
| 0.0183 | 6.8182 | 2700 | 0.1152 | 0.9749 |
| 0.0021 | 7.0707 | 2800 | 0.1368 | 0.9731 |
| 0.046 | 7.3232 | 2900 | 0.0900 | 0.9794 |
| 0.033 | 7.5758 | 3000 | 0.1014 | 0.9785 |
| 0.0354 | 7.8283 | 3100 | 0.0968 | 0.9767 |
| 0.0026 | 8.0808 | 3200 | 0.1217 | 0.9731 |
| 0.0002 | 8.3333 | 3300 | 0.0828 | 0.9794 |
| 0.0006 | 8.5859 | 3400 | 0.0926 | 0.9794 |
| 0.0006 | 8.8384 | 3500 | 0.1001 | 0.9794 |
| 0.0006 | 9.0909 | 3600 | 0.0863 | 0.9848 |
| 0.0633 | 9.3434 | 3700 | 0.0911 | 0.9803 |
| 0.0009 | 9.5960 | 3800 | 0.0941 | 0.9821 |
| 0.0247 | 9.8485 | 3900 | 0.0988 | 0.9785 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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