Challenge has Ended

COCO LITE Object Detection AI Challenge

The aim of this task is to develop an object detector and classify the given images.


Bounty for Rank 1
89 of 100 Participants
Organized By Dockship

Welcome to COCO LITE Object Detection AI Challenge!

The aim of the challenge is to train an object detection model on the given dataset. Currently provided dataset is a subset of COCO dataset. COCO is a large-scale object detection, segmentation, and captioning dataset.

The training data includes images and box annotation co-ordinates provided in the form of csv. More details regarding submissions can be found in the :"Submission Guidelines" section.

NOTE: The model may only be trained on given training data, no additional data can be used for training. Although, you may freely use techniques such as Image Augmentation.

Citation: COCO Dataset

The provided dataset includes two directories "training" (contains 15,000 training images) and "testing" (contains 6,001 test images) and two files named "train.csv" (containing training annotations) and partial.csv (partially pre-filled attributes for submission.). "Partial.csv" contains 8 columns in the follow order:

  • filename
  • width
  • height
  • class
  • xmin
  • ymin
  • xmax
  • ymax

The final output should be:

Single file "output.csv" containing 9 attributes: filename (name of images), width (width of image), height (height of image), class, xmin, ymin, xmax, ymax (bounding box co-ordinates) and conf (confidence of prediction).

The order of the attributes must be kept exactly the same, refer to partial.csv for help.

Please note carefully: Each row MUST contain details of single object only. Hence in case of multiple objects in an image they have to be inserted as each row separately with same filename.

A sample screenshot of the "output.csv" is given below for reference.


Quick Tip: You may use partial.csv and insert prediction values within it, as it contains pre-filled values for filename, height and width attributes. Remember to create additional column for confidence ("conf").

How to make a submission?

  1. Click on "My Submission"
  2. On the next page, click on "+ New Submission"
  3. Upload your CSV in the next page and click on "Submit for Review"

Please note:

  1. You must submit your CSV file by uploading the CSV in the "My Submissions" section of this challenge.
  2. Your submission will be auto graded and you will be able to see your results instantly.
  3. If there is any error in the submission, your final score will be marked as 0.


  1. mAP, Intersection over Union (IoU)


  1. We are using automatic anti-cheat system. Our system flags submission which have low trust scores. Manually modifying your submission CSV (via comparison of any form etc.), using image comparison techniques (pixel matching, file size matching etc,), in any form, can lead to your disqualification without any notice.
  2. Submission must not include copyrighted code. If violation is found, submission will be rejected.
  3. The submission should be in a proper format as described by "Submission Guidelines".
  4. Late submission will not be accepted beyond provided deadline (Indian Standard Time).

NOTE: We may request for the code files if there is any discrepancy in your score. Your score will be marked invalid or you can be disqualified if the request for code is not fulfilled by you.


The winner of the hackathon will be awarded INR 8,000.

The Top 3 participants will receive goodies from Dockship.

The goodies will include:

  • T-shirt
  • Premium Laptop Bag
  • Personalized Mugs
  • Personalized Notepads
  • Laptop Stickers

All the participants with "Successful Submission" will receive certificates from "Dockship".

How do I apply for this Challenge?
How do I download the dataset?
Can I make multiple submissions?
Where will the results be declared?
Can we apply as a team?
I've other queries, where can I get support?
Challenge Announced
02-Sept-2020, 12:42 pm IST
10-Sept-2020, 6:00 pm IST
Challenge Started
Application Deadline
08-Nov-2020, 6:00 pm IST
10-Nov-2020, 6:00 pm IST
Challenge Ended