Awesome

Ship Detection

This repository contains modules with trained U-Net architecture for solving image segmentation problem presented in Airbus Ship Detection Challenge as well as simple web-application proposing interactive demo of the model. Let us discuss the directories in more details.

model

This directory containes modules constructing, training and inferencing the model. First of all, in EDA.ipynb, one may find, as the name suggests, exploratory data analysis with the main emphasis made on the imbalance of chosen dataset and thus recommendation on reducing dataset to grasp more foreground of masks (ships).

In dataset.py, the transformation from run-length encode format (RLE) of masks to matrix-view is performed along with the definition of dataset generator using keras.utils.Sequence as base class. Also, the image size was reduced here from (768, 768) to (256, 256) due to the computational limitations of local training. One might change the shape here ot consider training on full-resolution images.

In model.py, the architecture of the model is presented as stated in the classical U-Net paper with slight modification on the input and output layers to fit dataset shape (as well as the number of applied convolutions, reducing their number thus reducing the number of parameters).

In train.py, the training pipline is set up with additional callbacks including early stopping, wandb monitoring, reduce of learning rate, checkpoint save in .h5 format and optimizer choice. Moreover, in train_utils.py additional functions are implemented: weighted binary cross-entropy loss (to deal with imbalance, more attention is pointed towards segmentation mask incorrected prediction, or, simply, false negative cases). Dice score calculation is also implemented here.

In predict.py one can find simple procedure for prediction implementation with the hard margin being set up at the level of 0.5.

In evaluate.py accuracy and dice scores calculations are wrapped up in the function.

All together is being combined and used in module interactive_model.ipynb with the presented results on the test dataset.

app

This directory is devoted to the web-demo of the model. In main.py, with the usage of FastAPI and uvicorn, one can find the endpoints implementation which make use of additional prediction functions which locate in predict.py. In templates folder, the html, css and extra js defitions are located.

config

This directory contains requirements text files with needed dependecies to successfully run the model block or the app one.

Web-Application Setup

In order to run the application, one should follow simple enough procedure.

1. Get docker on your desired computational resource.
2. Run bash build_image_app.sh being in the main folder. This file contains bash-script to build the app image from Dockerfile definition in docker/app.
3. Run docker compose up being in the main folder. This file defines what docker images should be activated (only the app one).
4. Voila! Get on 0.0.0.0:8000 and enjoy the solution!

Here is the example result of the web-application.