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Pervasive Artificial Intelligence Research (PAIR) Labs, National Chiao Tung University (NCTU), Taiwan

The Pervasive AI Research (PAIR) Labs, a group of national research labs funded by the Ministry of Science and Technology, Taiwan, is commissioned to achieve academic excellence, nurture local AI talents, build international linkage, and develop pragmatic approaches in the areas of applied AI technologies toward services, products, workflows, and supply chains innovation and optimization. PAIR is constituted of 18 distinguished research institutes in Taiwan to conduct research in various of applied AI areas.

Industrial Partner


MediaTek Inc. is a Taiwanese fabless semiconductor company that provides chips for wireless communications, high-definition television, handheld mobile devices like smartphones and tablet computers, navigation systems, consumer multimedia products and digital subscriber line services as well as optical disc drives. MediaTek is known for advances in multimedia, AI and expertise delivering the most power possible – when and where needed. MediaTek’s chipsets are optimized to run cool and super power-efficient to extend battery life. Always a perfect balance of high performance, power-efficiency, and connectivity.
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Academic Partner

Intelligent Vision System (IVS) Lab, National Chiao Tung University (NCTU), Taiwan

The Intelligent Vision System (IVS) Lab at National Chiao Tung University is directed by Professor Jiun-In Guo. We are tackling practical open problems in autonomous driving research, which focuses on intelligent vision processing systems, applications, and SoC exploiting deep learning technology.
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Wistron-NCTU Embedded Artificial Intelligence Research Center

Sponsored by Wistron and founded in 2020 September, Wistron-NCTU Embedded Artificial Intelligence Research Center (E-AI RDC) is a young and enthusiastic research center leaded by Prof. Jiun-In Guo (Institute of Electronics, National Chiao Tung University) aiming at developing the key technology related to embedded AI applications, ranging from AI data acquisition and labeling, AI model development and optimization and AI computing platform development with the help of easy to use AI toolchain (called ezAIT). The target applications cover AIoT, ADAS/ADS, smart transportation, smart manufacturing, smart medical imaging, and emerging communication systems. In addition to developing the above-mentioned technology, E-AI RDC will also collaborate with international partners as well as industrial partners in cultivating the talents in the embedded AI field to further enhance the industrial competitiveness in Taiwan Industry.


Object detection in the computer vision area has been extensively studied and making tremendous progress in recent years using deep learning methods. However, due to the heavy computation required in most deep learning-based algorithms, it is hard to run these models on embedded systems, which have limited computing capabilities. In addition, the existing open datasets for object detection applied in ADAS applications usually include pedestrian, vehicles, cyclists, and motorcycle riders in western countries, which is not quite similar to the crowded Asian countries like Taiwan with lots of motorcycle riders speeding on city roads, such that the object detection models training by using the existing open datasets cannot be applied in detecting moving objects in Asian countries like Taiwan.

In this competition, we encourage the participants to design object detection models that can be applied in Taiwan’s traffic with lots of fast speeding motorcycles running on city roads along with vehicles and pedestrians. The developed models not only fit for embedded systems but also achieve high accuracy at the same time.

This competition is divided into two stages: qualification and final competition.

  • Qualification competition: all participants submit their answers online. A score is calculated. The top 15 teams would be qualified to enter the final round of the competition.

  • Final competition: the final score will be evaluated on MediaTek Dimensity 1000 Series platform for the final score.

The goal is to design a lightweight deep learning model suitable for constrained embedded system design to deal with traffic in Asian countries like Taiwan. We focus on detection accuracy, model size, computational complexity, performance optimization and the deployment on MediaTek’s Dimensity 1000 platform.

With MediaTek’s Dimensity 1000 platform and its heterogeneous computing capabilities such as CPUs, GPUs and APUs (AI processing units) embedded into the system-on-chip products, developers are provided the high performance and power efficiency for building the AI features and applications. Developers can target these specific processing units within the system-on-chip or, they can also let MediaTek NeuroPoint SDK intelligently handle the processing allocation for them.

Given the test image dataset, participants are asked to detect objects belonging to the following four classes {pedestrian, vehicle, scooter, bicycle} in each image, including class, bounding box, and confidence.


According to the points of each team in the final evaluation, we select the highest three teams for regular awards.

  1. Champion: $USD 1,500

  2. 1st Runner-up: $USD 1,000

  3. 2nd Runner-up: $USD 750

Special Awards

  1. Best accuracy award – award for the highest mAP in the final competition: $USD 200;

  2. Best bicycle detection award – award for the highest AP of bicycle recognition in the final competition: $USD 200;

  3. Best scooter detection award – award for the highest AP of scooter recognition in the final competition: $USD 200;

All the award winners must agree to submit contest paper and attend the ACM ICMR2021 Grand Challenge PAIR Competition Special Session to present their work.

Activity time

1/4/2021Qualification Competition Start Date
Date to Release Public Testing Data
3/1/2021Date to Release Private Testing Data for Qualification
3/8/2021 12:00 PM UTCQualification Competition End Date
3/9/2021 12:00 AM UTCFinalist Announcement
3/9/2021Final Competition Start Date
3/15/2021Date to Release Private Testing Data for Final
3/22/2021 12:00 PM UTCFinal Competition End Date
4/12/2021 12:00 PM UTCAward Announcement

Evaluation Criteria

  • Qualification Competition

    The grading rule is based on MSCOCO object detection rule.

    • The mean Average Precision (mAP) is used to evaluate the result.

    • Intersection over union (IoU) threshold is set at 0.5.

    • Model size should be less than 200MB.

    The resulting average precision (AP) of each class will be calculated and the mean AP (mAP) over all classes is evaluated as the key metric.

    Besides, during the qualification competition period, each team has to submit a team composition document, including team name, leader, team members, affiliation, and contact information, etc.

  • Final Competition

    • Mandatory Criteria
      • Accuracy of final submission cannot be 5% lower (include) than their submitted model of qualification.

      • The summation of Preprocessing & Postprocessing time of final submission cannot be 50% slower (include) than the inference time of the main model. (evaluated on the host machine)

    • [Host] Accuracy(mAP) – 40%

      The team with the highest accuracy will get the full score (40%) and the team with the lowest one will get zero. The rest teams will get scores directly proportional to the mAP difference. 

    • [Host] Model size (number of parameters * bit width used in storing the parameters) – 15%

      The team with the smallest model will get the full score (15%) and the team with the largest one will get zero. The rest teams will get scores directly proportional to the model size difference.

    • [Host] Model Computational Complexity(GOPs/frame) – 15%

      The team with the smallest GOP number per frame will get the full score (15%) and the team with the largest one will get zero. The rest teams will get scores directly proportional to the GOP value difference.

    • [Device] Speed on MediaTek Dimensity 1000 Series Platform – 30%

      The team with a single model (w/o Preprocessing & Postprocessing) to complete the detection task in the shortest time will get the full score (30%) and the team that takes the longest time will get zero score. The rest teams will get scores directly proportional to the execution time difference.

    • The evaluation procedure will be toward the overall process from reading the private testing dataset in final to completing submission.csv file, including parsing image list, loading images, and any other overhead to conduct the detection through the testing dataset.

[1] Average Precision (AP):

[2] intersection over union (IoU):


Coordinator Contacts

Ted Kuo,

Jenq-Neng Hwang,

Jiun-In Guo,

Po-Yu Chen,

Hsien-Kai Kuo,

Chia-Chi Tsai,


  • Team mergers are not allowed in this competition.
  • Each team can consist of a maximum of 6 team members.
  • The task is open to the public. Individuals, institutions of higher education, research institutes, enterprises, or other organizations can all sign up for the task.
  • A leaderboard will be set up and make available publicly.
  • Multiple submissions are allowed before the deadline and the last one will be used to enter the final qualification consideration.
  • The upload date/time will be used as the tiebreaker.
  • Privately sharing code or data outside of teams is not permitted. Its okay to share code if made available to all participants on the forums.
  • Personnel of IVSLAB team are not allowed to participate in the task.
  • A common honor code should be observed. Any violation will be disqualified.