●  In India, over 60% of the population consumes fish. The World Health Organization emphasises the importance of the health of fish because fish is consumed by the majority of the human population. Also, WHO recommends regular monitoring of fish health in the fresh water aqua farms to ensure food safety.

●   In this regard, WHO suggests the systematic monitoring of fish health within freshwater aquafarms to uphold rigorous food safety standards. Such monitoring measures can be empowered by fish key point detection technology, facilitating precise and efficient oversight of fish health parameters. 

●  Fish biomass estimation through vision based approach aids the aqua farmers to harvest the fish in stipulated time slots. In aquaculture settings, accurate estimation of fish biomass is essential for optimizing feeding regimes, monitoring growth rates, and assessing overall fish health. Computer vision systems can automate the process of monitoring fish populations in aquaculture facilities, reducing labor costs and improving efficiency. Fish key point detection is the primary step for the vision-based fish biomass estimation process.

● Teams should consist of maximum four members each, with the designation of a team lead for effective communication purposes. 

●    The dataset will exclusively be accessible to teams that have completed the registration process. 

●  Participants are required to submit their algorithm's source code, authored in Python, and adequately commented. Additionally, teams must provide a comprehensive summary of their approach and algorithms in a written document. A demo script enabling the execution of the proposed solution on a test video must be included for further evaluation.

●     Participants must disclose the inference time of their code, utilized as an evaluation metric, and furnish details regarding the system specifications on which their code was developed 

●   Fair practice is essential. Violation could lead to disqualification of entire team.