A test example to identify Dog's breed, "Dogs breed detector", as example for DEEPaaS API.
DEEP Open Catalog entry: DEEP Open Catalog
Dogs breed detector is originally forked from udacity/dogs-project, dataset comes from dog dataset.
The project applies Transfer learning for dog's breed identification, implemented with Tensorflow and Keras:
From a pre-trained model (VGG16 | VGG19 | Resnet50 | InceptionV3 | Xception) the last layer is removed, then a new FC classification layer is added, which is trained. All images first pass through the pre-trained network and converted into the tensor with the shape of the 'before-last' layer of the pre-trained network, into so-called 'bottleneck_features'. These bottleneck_features are used then as input for the FC classification network.
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data <- Data placeholde
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── docker <- Directory for Dockerfile(s)
│
├── models <- Trained and serialized models, model predictions, or model summaries
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├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials (if many user development),
│ and a short `_` delimited description, e.g.
│ `1.0-jqp-initial_data_exploration.ipynb`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
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├── requirements-dev.txt <- The requirements file for the development environment
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├── test-requirements.txt <- The requirements file for the test environment
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├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
├── setup.cfg <- makes project pip installable (pip install -e .) so dogs_breed_det can be imported
├── setup.py <- makes project pip installable (pip install -e .) so dogs_breed_det can be imported
├── dogs_breed_det <- Source code for use in this project.
│ ├── __init__.py <- Makes dogs_breed_det a Python module
│ │
│ ├── dataset <- Scripts to download or generate data
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ predictions
│ │
│ └── tests <- Scripts to perfrom code testing + pylint script
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the cookiecutter data science project template. #cookiecutterdatascience