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--- |
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license: mit |
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tags: |
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- vision |
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- image-classification |
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- tensorflow |
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pipeline_tag: image-classification |
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library_name: keras |
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datasets: |
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- svnfs/depth-of-field |
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widget: |
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- src: https://huggingface.co/datasets/svnfs/depth-of-field/blob/main/data/0/-1a83VD65ss.jpg |
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example_title: Shallow DoF |
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- src: https://huggingface.co/datasets/svnfs/depth-of-field/blob/main/data/1/007R8JewpwU.jpg |
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example_title: Deep DoF |
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--- |
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# Bokeh (ボケ Japanese word for blur) |
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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 |
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## Model description |
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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 $0.0001$. |
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It has 3.632 trainable parameters, 8 convolution filters are used for the network's input, with $7\times7$ kernel size. |
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## Training data |
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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. |
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### BibTeX entry and citation info |
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``` |
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@article{sniafas2021, |
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title={DoF: An image dataset for depth of field classification}, |
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author={Niafas, Stavros}, |
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doi= {10.13140/RG.2.2.17217.89443}, |
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url= {https://www.researchgate.net/publication/355917312_Photography_Style_Analysis_using_Machine_Learning} |
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year={2021} |
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} |
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``` |
