it is now maintained in https://github.com/ai4os-hub/dogs-breed-detector/
An application to identify Dog's breed, "Dogs breed detector", using deep learning. 133 breeds are known.
DEEPaaS API V2 is used to access the model functionality.
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) the last layer is removed, then new FC classification layers are 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 placeholder
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries (placeholder)
│
├── 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
│
├── 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