---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ai_art_exp1_vit_final
  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. -->

# ai_art_exp1_vit_final

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Accuracy: {'accuracy': 0.9946666666666667}
- Overall Accuracy: 0.9947
- Loss: 0.0231
- Human Accuracy: 0.99
- Ld Accuracy: 0.998
- Sd Accuracy: 0.996

## 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 | Accuracy                         | Overall Accuracy | Validation Loss | Human Accuracy | Ld Accuracy | Sd Accuracy |
|:-------------:|:------:|:----:|:--------------------------------:|:----------------:|:---------------:|:--------------:|:-----------:|:-----------:|
| 0.198         | 0.992  | 93   | {'accuracy': 0.9506666666666667} | 0.9507           | 0.1906          | 0.8548         | 0.9981      | 0.9959      |
| 0.0647        | 1.9947 | 187  | {'accuracy': 0.9793333333333333} | 0.9793           | 0.0811          | 0.9489         | 0.9923      | 0.9959      |
| 0.0395        | 2.9973 | 281  | {'accuracy': 0.988}              | 0.988            | 0.0567          | 0.9734         | 0.9904      | 1.0         |
| 0.069         | 4.0    | 375  | {'accuracy': 0.9933333333333333} | 0.9933           | 0.0399          | 0.9816         | 1.0         | 0.9980      |
| 0.0456        | 4.992  | 468  | {'accuracy': 0.9946666666666667} | 0.9947           | 0.0309          | 0.9877         | 1.0         | 0.9959      |
| 0.0324        | 5.9947 | 562  | {'accuracy': 0.9906666666666667} | 0.9907           | 0.0444          | 0.9734         | 1.0         | 0.9980      |
| 0.0136        | 6.9973 | 656  | {'accuracy': 0.996}              | 0.996            | 0.0234          | 0.9939         | 1.0         | 0.9939      |
| 0.0137        | 8.0    | 750  | {'accuracy': 0.9953333333333333} | 0.9953           | 0.0218          | 0.9898         | 0.9962      | 1.0         |
| 0.0105        | 8.992  | 843  | {'accuracy': 0.9953333333333333} | 0.9953           | 0.0222          | 0.9877         | 1.0         | 0.9980      |
| 0.0111        | 9.92   | 930  | {'accuracy': 0.9986666666666667} | 0.9987           | 0.0122          | 0.9980         | 0.9981      | 1.0         |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1