As marine debris becomes a critical global issue, leveraging AI technology to enhance environmental monitoring is a major international trend. To accelerate the digital transformation of marine governance, this "International AI Challenge for Marine Debris Image Recognition" will release a high-quality marine debris image dataset. We invite outstanding professionals from global academia, research institutions, industry, and tech communities to develop high-precision object detection models. Your participation will drive innovation in marine monitoring and promote practical AI applications in environmental governance.
This competition is a vital platform connecting global innovation. Whether you have a background in AI, data analysis, computer vision, marine science, or are simply passionate about sustainability, you are welcome to join. Teams will not only have the opportunity to win prize money and gain international exposure but will also use real-world data to transform technical innovation into tangible environmental impact. Join us in tackling marine sustainability challenges with AI and building a smarter future together!
Activity times are based on the Taiwan time zone (GMT+8)
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 19+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
5. Category Merging Rules.
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 examplesThe 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)