Dog's breed detector
A test example to identify Dog's breed, "Dogs breed detector", as example for DEEPaaS API.
DEEP Open Catalog entry: DEEP Open Catalog
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 │ ├── 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 │ ├── requirements-dev.txt <- The requirements file for the development environment │ ├── test-requirements.txt <- The requirements file for the test environment │ ├── 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