readme: ---
  license: mit
  tags:
  - vision
  - image-classification
  - tensorflow
  pipeline_tag: image-classification
  library_name: keras
  datasets:
  - svnfs/depth-of-field
  widget:
  - src: https://huggingface.co/datasets/svnfs/depth-of-field/blob/main/data/0/-1a83VD65ss.jpg
    example_title: Shallow DoF
  - src: https://huggingface.co/datasets/svnfs/depth-of-field/blob/main/data/1/007R8JewpwU.jpg
    example_title: Deep DoF  
  ---
  # Bokeh (ボケ Japanese word for blur) 
  
  Bokeh model is based on a densenet like architecture trained on Unsplash images at 300x200 resolution. It classifies whether an photo is capture with bokeh producing a shallow depth of field
  
  ## Model description
  
  Bokeh model is based on a DenseNet architecture. The model is trained with a mini-batch size of 32 samples with Adam optimizer and a learning rate $0.0001$.
  It has 3.632 trainable parameters, 8 convolution filters are used for the network's input, with $7\times7$ kernel size.
  
  ## Training data
  
  The bokeh model is pretrained on [depth-of-field](https://huggingface.co/datasets/svnfs/depth-of-field) dataset, a dataset consisted of 1200 images and 2 classes manually annotated.
  ### BibTeX entry and citation info
  
  ```
  @article{sniafas2021,
    title={DoF: An image dataset for depth of field classification},
    author={Niafas, Stavros},
    doi= {10.13140/RG.2.2.17217.89443},
    url= {https://www.researchgate.net/publication/355917312_Photography_Style_Analysis_using_Machine_Learning}
    year={2021}
  }
  ```