---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-mobile-eye-tracking-dataset-v2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9898089171974522
---

<!-- 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-base-patch16-224-in21k-mobile-eye-tracking-dataset-v2

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0542
- Accuracy: 0.9898

## 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: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.024         | 0.99  | 73   | 0.0769          | 0.9809   |
| 0.0236        | 1.99  | 147  | 0.1111          | 0.9745   |
| 0.0172        | 3.0   | 221  | 0.0542          | 0.9898   |
| 0.0114        | 4.0   | 295  | 0.0630          | 0.9885   |
| 0.0051        | 4.99  | 368  | 0.0674          | 0.9860   |
| 0.0044        | 5.99  | 442  | 0.0640          | 0.9885   |
| 0.0037        | 7.0   | 516  | 0.0646          | 0.9885   |
| 0.0034        | 8.0   | 590  | 0.0652          | 0.9885   |
| 0.0032        | 8.99  | 663  | 0.0656          | 0.9885   |
| 0.0032        | 9.9   | 730  | 0.0657          | 0.9885   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3