Airbus Ship Detection Challenge

This repository contains my research of the Airbus Ship Detection Challenge.

My goal was to develop a ship-detecting convolutional network along with a training/optimization pipeline and then build a production deployment of the model using REST API.

Version: 0.1.0

Project

The project consists of two main packages core and service.

The core package defines the model architecture, optimization and training process.

The service package serves the model via a simple REST API. The package consists of a lightweight Flask application implementing the API and a batch processing backend. Both the app and the backend run in independent processes and communicate using Redis.

Installation

To install the package, use the setup.py.

python setup.py install

Command Line Interface

The package exposes a CLI containing two subprasers:

service for running the model service
training for running the training pipeline

python -m asdc.main service -m /path/to/the/model.h5

The -m parameter specifies the path to a h5 model file. Before running the service, make sure there is a Redis instance running on localhost:6379.

python -m asdc.main training -c /path/to/the/training.cfg

The -c parameter specifies the path to a config file defining all the training parameters. Example of the config can be found in config/training.cfg.

Building the Image

The model service can be build as a Docker image. The image contains everything needed (the package itself, redis server and an h5 file with a trained model). The API is exposed via port 5000.

To build the image:

sudo docker build . -t asdc

To run the container:

sudo docker run -p 5000:5000 -t asdc

Using the API

The model service exposes an API entrypoint host:5000/detect that accepts POST requests containing JSON data in the following form:

{"image": "iVBORw0KGgoAAAANSUhEUgAAAA"}

The image field contains a base64 encoded image. The image must be a squared-shaped RGB jpg or png.

The service returns a JSON response with a probability of a ship being present in the image.

{"prediction": 0.55}

In case of an error, the response contains an error message.

{"error": "image_not_compatible"}

Model Performance

The model and the training pipeline support region-wise classification where each image region is classified independently, the output is an image mask specifying ship presence in each of the regions.

The mask can have an arbitrary resolution, from pixel-wise to global binary classification.

The current model is using MASK_SIZE = (1, 1) (binary image classifier).

The model achieves accuracy of 0.74 on a balanced dataset with 0.5 share of positive labels. ROC AUC score is 0.82.

Tests

Unit tests can be run using pytest. FutureWarning and DeprecationWarning warnings are silenced.

python -m pytest asdc/tests

jancervenka/airbus-ship-detection