Airborne Object Tracking Challenge Starter Kit

This repository is the main Airborne Object Tracking challenge Submission template and Starter kit!

Clone the repository to compete now!

This repository contains:

Documentation on how to submit your agent to the leaderboard
The procedure for best practices and information on how we evaluate your agent, etc.
Starter code for you to get started!

Competition Procedure
How to access and use dataset
How to start participating
How do I specify my software runtime / dependencies?
What should my code structure be like ?
How to make submission
Other concepts
Important links

Competition Procedure

The main task of the competition is to detect a collision threat reliably. In this challenge, you will train your agents locally and then upload them to AIcrowd (via git) to be evaluated.

The following is a high level description of how this round works

Sign up to join the competition [on the AIcrowd website].(https://www.aicrowd.com/challenges/airborne-object-tracking-challenge)
Clone this repo and start developing your solution.
Train your models to detect objects and write inference code in test.py.
Submit your trained models to AIcrowd Gitlab for evaluation (full instructions below). The automated evaluation setup will evaluate the submissions against the test dataset to compute and report the metrics on the leaderboard of the competition.

How to access and use dataset

The starter kit contains dataset exploration notebooks and helper functions to access the dataset. You can check the instructions for the same here: 👉 DATASET.md.

How to start participating

Setup

Add your SSH key to AIcrowd GitLab

You can add your SSH Keys to your GitLab account by going to your profile settings here. If you do not have SSH Keys, you will first need to generate one.

Clone the repository

git clone git@gitlab.aicrowd.com:amazon-prime-air/airborne-detection-starter-kit.git

Install competition specific dependencies!

cd airborne-detection-starter-kit
pip3 install -r requirements.txt

Run local exploration notebook present in data/dataset-playground.ipynb using jupyter notebook command locally.
Try out random prediction codebase present in test.py.

How do I specify my software runtime / dependencies ?

We accept submissions with custom runtime, so you don't need to worry about which libraries or framework to pick from.

The configuration files typically include requirements.txt (pypi packages), environment.yml (conda environment), apt.txt (apt packages) or even your own Dockerfile.

You can check detailed information about the same in the 👉 RUNTIME.md file.

What should my code structure be like ?

Please follow the example structure as it is in the starter kit for the code structure. The different files and directories have following meaning:

.
├── aicrowd.json           # Submission meta information - like your username
├── apt.txt                # Packages to be installed inside docker image
├── data                   # Your local dataset copy - you don't need to upload it (read DATASET.md)
├── requirements.txt       # Python packages to be installed
├── test.py                # IMPORTANT: Your testing/inference phase code, must be derived from AirbornePredictor (example in test.py)
└── utility                # The utility scripts to provide smoother experience to you.
    ├── docker_build.sh
    ├── docker_run.sh
    ├── environ.sh
    └── verify_or_download_data.sh

Finally, you must specify an AIcrowd submission JSON in aicrowd.json to be scored!

The aicrowd.json of each submission should contain the following content:

{
  "challenge_id": "airborne-detection-challenge",
  "authors": ["your-aicrowd-username"],
  "description": "sample description about your awesome agent",
  "license": "MIT",
  "gpu": true
}

This JSON is used to map your submission to the challenge - so please remember to use the correct challenge_id as specified above.

Please specify if your code will use a GPU or not for the evaluation of your model. If you specify true for the GPU, GPU will be provided and used for the evaluation.

How to make submission

👉 SUBMISSION.md

Best of Luck 🎉 🎉

Other Concepts

Time constraints

You need to make sure that your model can predict airborne objects for each flight within 1000 seconds, otherwise the submission will be marked as failed. (need revision on timeouts based on budget)