---
base_model: microsoft/dit-base-finetuned-rvlcdip
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: dit-base-finetuned-rvlcdip-finetuned-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: 0.8333333333333334
---

<!-- 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. -->

# dit-base-finetuned-rvlcdip-finetuned-mobile-eye-tracking-dataset-v2

This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4029
- Accuracy: 0.8333

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.86  | 3    | 0.6939          | 0.4792   |
| No log        | 2.0   | 7    | 0.6927          | 0.5417   |
| 0.6938        | 2.86  | 10   | 0.6986          | 0.4167   |
| 0.6938        | 4.0   | 14   | 0.6959          | 0.4583   |
| 0.6938        | 4.86  | 17   | 0.6798          | 0.6875   |
| 0.684         | 6.0   | 21   | 0.6890          | 0.5208   |
| 0.684         | 6.86  | 24   | 0.6768          | 0.5833   |
| 0.684         | 8.0   | 28   | 0.6637          | 0.6667   |
| 0.6772        | 8.86  | 31   | 0.7099          | 0.5      |
| 0.6772        | 10.0  | 35   | 0.6467          | 0.6042   |
| 0.6772        | 10.86 | 38   | 0.6059          | 0.6875   |
| 0.6283        | 12.0  | 42   | 0.5860          | 0.7292   |
| 0.6283        | 12.86 | 45   | 0.5670          | 0.7708   |
| 0.6283        | 14.0  | 49   | 0.5677          | 0.6875   |
| 0.5712        | 14.86 | 52   | 0.5453          | 0.6875   |
| 0.5712        | 16.0  | 56   | 0.4991          | 0.7708   |
| 0.5712        | 16.86 | 59   | 0.4830          | 0.75     |
| 0.5485        | 18.0  | 63   | 0.4485          | 0.7917   |
| 0.5485        | 18.86 | 66   | 0.4268          | 0.8542   |
| 0.506         | 20.0  | 70   | 0.4588          | 0.7708   |
| 0.506         | 20.86 | 73   | 0.4412          | 0.75     |
| 0.506         | 22.0  | 77   | 0.4099          | 0.8333   |
| 0.4636        | 22.86 | 80   | 0.4005          | 0.875    |
| 0.4636        | 24.0  | 84   | 0.4039          | 0.8333   |
| 0.4636        | 24.86 | 87   | 0.4040          | 0.8333   |
| 0.4453        | 25.71 | 90   | 0.4029          | 0.8333   |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.13.3