---
library_name: transformers
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-large-ai-or-not
  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. -->

# vit-large-ai-or-not

This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1039
- Accuracy: 0.9581

## 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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3925        | 0.1808 | 200  | 0.4045          | 0.8786   |
| 0.2803        | 0.3617 | 400  | 0.2386          | 0.9044   |
| 0.2235        | 0.5425 | 600  | 0.1893          | 0.9173   |
| 0.217         | 0.7233 | 800  | 0.1597          | 0.9398   |
| 0.1865        | 0.9042 | 1000 | 0.1413          | 0.9420   |
| 0.1309        | 1.0850 | 1200 | 0.1474          | 0.9517   |
| 0.1008        | 1.2658 | 1400 | 0.1914          | 0.9420   |
| 0.0793        | 1.4467 | 1600 | 0.1557          | 0.9441   |
| 0.0804        | 1.6275 | 1800 | 0.2301          | 0.9313   |
| 0.0814        | 1.8083 | 2000 | 0.1039          | 0.9581   |
| 0.0446        | 1.9892 | 2200 | 0.1124          | 0.9635   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1