HackAIthon 2026

Hack[AI]thon is a 24-hour national-level AI hackathon organized by Sphere Hive, KVG College of Engineering in collaboration with Startup Lab, Joy University. This hackathon focuses on a problem statement based on Data-Centric AI, where participants improve model performance by improving the dataset rather than modifying the model architecture. Participants will work on an image classification challenge using the 3LC platform, where they will analyze embeddings, strategically label data, retrain models, and compete on a live leaderboard. The event is designed to simulate real-world AI workflows, emphasizing data quality, experimentation, and iterative improvement. Challenge Overview Participants must build an image classification model using Data-Centric AI techniques. Model architecture is fixed No pretrained weights allowed Performance must be improved by data labeling and curation Final evaluation is based on accuracy on a hidden test dataset Eligibility Open to students and developers from all colleges Participants can join in teams Basic knowledge of Python / Machine Learning is recommended Participants must bring their own laptop and charger Team Format Team size: 2 to 4 members Cross-college teams are allowed Each team must submit one final solution Rules Only the provided dataset must be used External datasets are not allowed The model architecture must remain unchanged Pretrained weights are not permitted All work must be done during the hackathon duration Submissions must follow the required format Any form of plagiarism or unfair means will lead to disqualification Judges’ decision will be final Process Participants receive dataset and starter resources Train baseline model Analyze model behavior using 3LC dashboard (embeddings & metrics) Select and label important unlabeled samples Retrain model with improved dataset Submit predictions to leaderboard Final ranking based on accuracy and approach Submission Format Participants must submit: Prediction file (as per required format) Source code Brief explanation of approach 3LC workflow summary or screenshots GitHub repository link (if required) Evaluation Criteria Model accuracy on test dataset Data-centric approach and strategy Effective use of embeddings and labeling Experimentation and iteration Clarity of explanation and documentation Perks & Benefits ₹30,000 prize pool Certificates for all participants Free .xyz domain for participants Swag kits and goodies Networking with AI mentors and peers Hands-on experience with Data-Centric AI tools