BRAINBYTE: AI Model Compression Challenge

In a world where efficiency meets intelligence, welcome to BRAINBYTE, the ultimate AI Model Compression Challenge. Your mission? Compress machine learning models for faster inference, lower memory usage, and deployment on resource-constrained environments — all without sacrificing accuracy. Whether you love quantization, pruning, or knowledge distillation, this event is for AI engineers and ML lovers who believe smaller can be smarter.

🔧 Shrink it. Speed it. Still make it smart.

📅 Event Details

Event Date: 9 November 2023
Registration Deadline: 1 November 2023
Team Size: Solo or up to 4

💡 Challenge Structure

🧠 Round 1: Baseline to Byte-sized

Participants are provided with a pre-trained model and dataset.
🔹 Apply model compression techniques (quantization, pruning, weight sharing, etc.).
🔹 Measure impact on model size, latency, and accuracy.

⚡ Round 2: Knowledge Distillation Showdown

🧪 Use knowledge distillation to build a student model from a larger teacher model.
🔍 Evaluate trade-offs between model size and performance.

🧮 Round 3: Real-World Deployment Simulation

📱 Optimize a model for edge-device deployment (e.g., Raspberry Pi, smartphones).
⚖️ Deliver a balance of performance, inference time, and hardware efficiency.

🧑‍⚖️ Judging Criteria

✔️ Model Accuracy – Before and after compression
✔️ Size Reduction – Compressed vs baseline
✔️ Inference Speed – Time per prediction
✔️ Innovation in Technique – Unique approaches or combinations
✔️ Deployment Feasibility – Practicality for real-world use

🛠️ Tools & Libraries Allowed

TensorFlow Lite / ONNX / PyTorch Mobile
OpenVINO / TVM / TensorRT
Python, NumPy, Scikit-learn
Any open-source model compression toolkit

🌟 Why Participate?

🎯 Master edge AI deployment
🎯 Explore state-of-the-art ML efficiency techniques
🎯 Optimize real-world AI for mobile and IoT
🎯 Boost your ML portfolio with real, deployable solutions

👥 Who Can Join?

Machine Learning Engineers
AI Developers & Researchers
Data Scientists
Students exploring model optimization & deployment
Hackers who love shrinking things smartly 😄