Airbus Ship Detection Challenge

This is an open solution to the Airbus Ship Detection Challenge.

Our goals

We are building entirely open solution to this competition. Specifically: 1. Learning from the process - updates about new ideas, code and experiments is the best way to learn data science. Our activity is especially useful for people who wants to enter the competition, but lack appropriate experience. 1. Encourage more Kagglers to start working on this competition. 1. Deliver open source solution with no strings attached. Code is available on our GitHub repository :computer:. This solution should establish solid benchmark, as well as provide good base for your custom ideas and experiments. We care about clean code :smiley: 1. We are opening our experiments as well: everybody can have live preview on our experiments, parameters, code, etc. Check: Airbus Ship Detection Challenge :chart_with_upwards_trend: or screen below.

|Train and validation monitor :bar_chart:| |:---:| ||

Disclaimer

In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script :snake:.

How to start?

Learn about our solutions

1. Check Kaggle forum and participate in the discussions.
2. See solutions below:

| link to code | CV | LB | |:---:|:---:|:---:| |solution 1|0.541|0.573| |solution 2|0.661|0.679| |solution 3|0.694|0.696| |solution 4|0.722|0.703| |solution 5|0.719|0.725|

Start experimenting with ready-to-use code

You can jump start your participation in the competition by using our starter pack. Installation instruction below will guide you through the setup.

Installation (fast track)

1. Clone repository and install requirements (use Python3.5) pip3 install -r requirements.txt
2. Register to the neptune.ml (if you wish to use it)
3. Run experiment based on U-Net:

Cloud

bash neptune account login

Create project say Ships (SHIP)

Go to neptune.yaml and change:

yaml project: USERNAME/PROJECT_NAME to your username and project name

Prepare metadata and overlayed target masks It only needs to be done once

```bash neptune send --worker xs \ --environment base-cpu-py3 \ --config neptune.yaml \ prepare_metadata.py

```

They will be saved in the

yaml metadata_filepath: /output/metadata.csv masks_overlayed_dir: /output/masks_overlayed

From now on we will load the metadata by changing the neptune.yaml

yaml metadata_filepath: /input/metadata.csv masks_overlayed_dir: /input/masks_overlayed

and adding the path to the experiment that generated metadata say SHIP-1 to every command --input/metadata.csv

Let's train the model by running the main.py:

```bash neptune send --worker m-2p100 \ --environment pytorch-0.3.1-gpu-py3 \ --config neptune.yaml \ --input /SHIP-1/output/metadata.csv \ --input /SHIP-1/output/masks_overlayed \ main.py

```

The model will be saved in the:

yaml experiment_dir: /output/experiment

and the submission.csv will be saved in /output/experiment/submission.csv

You can easily use models trained during one experiment in other experiments. For example when running evaluation we need to use the previous model folder in our experiment. We do that by:

changing main.py

python CLONE_EXPERIMENT_DIR_FROM = '/SHIP-2/output/experiment'

and running the following command:

bash neptune send --worker m-2p100 \ --environment pytorch-0.3.1-gpu-py3 \ --config neptune.yaml \ --input /SHIP-1/output/metadata.csv \ --input /SHIP-1/output/masks_overlayed \ --input /SHIP-2 \ main.py

Local

Login to neptune if you want to use it bash neptune account login

Prepare metadata by running:

bash neptune run --config neptune.yaml prepare_metadata.py

Training and inference by running main.py:

bash neptune run --config neptune.yaml main.py

You can always run it with pure python :snake:

bash python main.py

Get involved

You are welcome to contribute your code and ideas to this open solution. To get started: 1. Check competition project on GitHub to see what we are working on right now. 1. Express your interest in particular task by writing comment in this task, or by creating new one with your fresh idea. 1. We will get back to you quickly in order to start working together. 1. Check CONTRIBUTING for some more information.

User support

There are several ways to seek help: 1. Kaggle discussion is our primary way of communication. 1. Submit an issue directly in this repo.

Releases

solution-2 2018-10-15 07:30:39

• added ship no ship model and 2 stage inference
• fixed pspnet
• decoupled encoders from models
• dropped mask to bbox postprocessing

solution-0 2018-10-10 13:37:44

• end to end pipelines
• 2 epochs
• no tweaks
• no postpro