Technology
8 months
USA
Companies that train large AI models often face the challenge of labeling massive datasets, a task that is both laborious and time-consuming. Traditional data labeling methods are inefficient, prone to errors, and require significant human effort, making it difficult to find skilled individuals to complete the task accurately and on time. This results in delayed AI model development and increased operational costs.
Susana Raj came up with a blurry idea to make image labeling for big AI models fun and easy for people who trained huge data AI models. It makes it easier for AI model trainers to find the right people for the job at the right time, which is crucial because image labeling can be a tough and time-consuming process.
Tezeract did an in-depth analysis of the idea and turned Minmini into a fully functional real-time solution AI-powered image labeling tool developed to address the challenges in labeling data for AI models. It serves data scientists, image labeling companies, and individuals looking to earn income through simple labeling tasks. By using object detection and automated image annotation software, Minmini automates up to 70% of the labeling process for large-scale AI models, significantly enhancing efficiency.
The platform features two main interfaces: a super admin panel for comprehensive monitoring and an admin panel for companies to create labeling contests, set monetary rewards, and prioritize tasks. Users can participate in image labeling, track their work, and earn money directly into their virtual wallets.
Minmini turns a traditionally repetitive task into an engaging experience by introducing contests and rewards. As an AI data labeling platform case study, it demonstrates how organizations can streamline large-scale model training while giving contributors a rewarding experience. This object detection case study showcases the transformation of data labeling into a dynamic process that benefits both companies and users, offering automation and improved productivity.
Ensuring precise object detection and automated image annotation is crucial. Developing algorithms that minimize errors and handle diverse image types accurately is a key challenge.
The platform must efficiently manage large volumes of data and simultaneous users. Optimizing for high performance and scalability while maintaining reliability is essential.
Creating and managing contests and rewards to keep users motivated poses a challenge. Ensuring the incentive system is effective and seamless is vital for high participation and user satisfaction.
We aligned with the business vision to develop a thorough project brief, which included extensive market research, competitor analysis, and relevant data. We then brought the concept to life by defining a detailed list of user stories and features to validate the core assumptions of the MVP.
We started by designing the key screens to show the client the product’s look. After refining the UX/UI through client feedback. Once these primary screens were finalized, we expanded the design to cover all additional screens and delivered the complete UX/UI for the product.
At this stage, our focus shifts to developing the AI solution for fashion brands by designing the architecture of the AI model and training it.
After the product launch, we collected feedback from end-users to refine and enhance the product. We introduced new iterations and features concurrently to ensure an optimal user experience.
The client can create multiple contests for labeling data by uploading images if they want.
Clients can set the priority of the contest based on its urgency data if they want to label data quickly more money will be charged
Users can earn money by labeling the images
Get a straight to the point opinion from someone that has been building award-winning Products for the past 10 years
We have worked with some of the best innovative ideas and brands in the world across industries.
We help businesses by delivering AI-powered Mobile and Web Apps built from scratch to deployment on platforms.
