---
license: apache-2.0
base_model: facebook/deit-tiny-distilled-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
  results: []
pipeline_tag: image-classification
datasets: tarekziade/docornot
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
  example_title: Savanna
---



# Results

This model is a fine-tuned version of [facebook/deit-tiny-distilled-patch16-224](https://huggingface.co/facebook/deit-tiny-distilled-patch16-224) on the [docornot](https://huggingface.co/datasets/tarekziade/docornot) dataset.

It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0




## Model description

This model is distilled Vision Transformer (ViT) model. 
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. 

## Intended uses & limitations

You can use this model to detect if an image is a picture or a document.

## Training procedure

Source code used to generate this model : https://github.com/tarekziade/docornot

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0           | 1.0   | 1600 | 0.0000          | 1.0      |


### Framework versions

- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2