metadata

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 on the 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