Commit babd359b authored by Siddhartha Laghuvarapu's avatar Siddhartha Laghuvarapu
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# NeurIPS 2021- AWS Deepracer AI Driving Olympics Challenge - Starter Kit
👉 [Challenge page](
This repository is the main AWS Deepracer AI Driving Olympics Challenge **Submission template and Starter kit**!
The AI Driving Olympics (AI-DO) is a series of embodied intelligence competitions in the field of autonomous vehicles.
The overall objective of the AI-DO is to provide accessible mechanisms for benchmarking progress in autonomy applied to the task of autonomous driving
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!
* **Baseline**: Baseline Models
# Table of Contents
1. [Competition Procedure](#competition-procedure)
2. [How to access and use dataset](#how-to-access-and-use-dataset)
3. [How to start participating](#how-to-start-participating)
4. [How do I specify my software runtime / dependencies?](#how-do-i-specify-my-software-runtime-dependencies-)
5. [What should my code structure be like ?](#what-should-my-code-structure-be-like-)
6. [How to make submission](#how-to-make-submission)
7. [:star: SiamMOT baseline](#submit-siammot-baseline)
8. [Other concepts and FAQs](#other-concepts)
9. [Important links](#-important-links)
# Competition Procedure
**The following is a high level description of how this round works**
1. **Sign up** to join the competition [on the AIcrowd website].(
2. **Clone** this repo and start developing your solution.
3. **Train** your models and writer code in ``.
4. [**Submit**](#how-to-submit-a-model) your trained models to [AIcrowd Gitlab]( for evaluation [(full instructions below)](#how-to-submit-a-model). 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 setup the environment
# How to start participating
## Setup
1. **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](
2. **Clone the repository**
git clone
3. **Install** competition specific dependencies!
cd airborne-detection-starter-kit
pip3 install -r requirements.txt
4. Try out the baseline model available in ``.
## 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 👉 [](/docs/ 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
├── requirements.txt # Python packages to be installed
├── # IMPORTANT: Your testing/inference phase code, must be derived from AirbornePredictor (example in
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": "evaluations-api-airborne",
"grader_id": "evaluations-api-airborne",
"authors": ["aicrowd-bot"],
"tags": "change-me",
"description": "Random prediction model for Airborne challenge",
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
👉 [](/docs/
**Best of Luck** :tada: :tada:
# Other Concepts
## Time constraints
You need to make sure that your model finishes evaluation in 1500 seconds, otherwise your evaluation will be marked failed.
## Local evaluation
You can also test end to end evaluation on your own systems.
## Hardware used for evaluations
We use g4dn instances to run your evaluations.
# 📎 Important links
💪  Challenge Page:
🗣️  Discussion Forum:
🏆  Leaderboard:
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