As global attention on marine debris intensifies, leveraging AI to enhance governance efficiency has become a prominent international trend. To accelerate this digital transformation, the National Academy of Marine Research (NAMR) has established an AI-Ready marine debris dataset covering 33+1 distinct categories. To this end, NAMR is hosting the "International AI Challenge on Marine Debris Image Recognition," officially releasing this high-quality dataset this July as a training set for machine learning models. The competition openly invites global talent from fields such as artificial intelligence, computer vision, and marine science to step up to the challenge. By developing high-precision object detection models, participants will turn technological innovation into tangible environmental impact, working together to shape a smarter, more sustainable future for marine governance.
Activity times are based on the Taiwan time zone (GMT+8)
Join our Online Information Session to master the competition rules, testing platform, cloud resources, and development environment!
Preliminary Round & Finals
The evaluation metric uses mean Average Precision (mAP)[1] at an Intersection over Union (IoU)[2] threshold of 0.5. A prediction bounding box is considered a True Positive (TP) if its IoU with the ground truth bounding box is greater than 0.5; otherwise, it is a False Positive (FP). Precision is then calculated based on TP and FP counts. The system evaluates the AP score for each object type and then averages the AP values across the 33+1 classes of marine debris objects to obtain the final mAP evaluation value, which determines the ranking. The system uses the COCO API[3] to calculate the mAP values.
Reference
[1] Average Precision (AP): en.wikipedia.org/…#Average_precision
[2] Intersection over Union (IoU): en.wikipedia.org/wiki/Jaccard_index
[3] COCO API: github.com/cocodataset/cocoapi
Best Lightweight Optimization Award
To encourage teams to develop models that balance practical application, high performance, and
low resource consumption, the "Best Lightweight Optimization Award" has been specially established. This
award utilizes the NetScore metric for comprehensive evaluation. All teams that successfully advance to
the Finals and submit a valid model complying with the regulations will automatically qualify for this
award.
The evaluation formula is defined as:Ω = 20 log₁₀(a² / (√p √m))
•𝑎(Model Accuracy): Measured as a percentage of mAP. For example, if the mAP is 0.85, then $a = 85$
•𝑝(Model Parameter Count / Size): Evaluated based on the model size in MB
•𝑚(Computational Complexity): Evaluated based on GFLOPs
Note: The slots for this award are calculated independently from the top three spots in the Finals (Core Awards). Participating teams are eligible to win both a Core Award and the Best Lightweight Optimization Award simultaneously.
Data Description
References:[1] MDImageNet:https://mdimagenet.namr.gov.tw/
Upload Format Instructions
The submission format must be submission.csv. Please list the predicted object bounding boxes in the uploaded file, with one row per object. The file must be saved in CSV format (comma-separated). The following explains the example fields for submission.csv:
Click to view submission.csv examples Upload Answers on AIdeaThe technical workshops will combine digital courses with in-person networking events, leading participants to deeply understand the characteristics of the marine debris image dataset, object detection techniques, and model development practices. Through expert sharing and hands-on guidance, teams will be assisted in mastering key competition skills, enhancing model development capabilities, and improving their performance.
(Coming Soon)