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Knowledge Distillation for Semantic Segmentation

A model compression system that distills knowledge from large teacher networks into lightweight student models for real-time semantic segmentation, achieving significant speedup with minimal accuracy loss.

Knowledge Distillation for Semantic Segmentation

Overview

This project implements knowledge distillation techniques to compress large semantic segmentation models into efficient student networks suitable for real-time inference. The system transfers learned representations from state-of-the-art teacher models (e.g., DeepLabV3+, SegFormer) to compact architectures, enabling deployment on edge devices and resource-constrained environments.

Problem

Semantic segmentation models with high accuracy are typically large and computationally expensive, making them unsuitable for real-time applications on mobile devices, embedded systems, or edge computing platforms. Direct model quantization or pruning often leads to significant accuracy degradation.

Solution

Knowledge distillation transfers the "soft" knowledge from teacher models to student networks through carefully designed loss functions that capture intermediate feature representations, attention maps, and output distributions. This allows student models to learn richer representations than training from scratch.

Architecture

  • Teacher-student framework with configurable architectures for both networks
  • Multi-level distillation losses capturing features at different network depths
  • Attention transfer mechanisms to preserve spatial understanding
  • Progressive distillation strategy for training stability
  • Evaluation pipeline comparing accuracy, inference speed, and model size

Implementation Details

  • Custom distillation loss functions combining feature matching, attention transfer, and output distillation
  • PyTorch implementation with support for various segmentation architectures
  • Efficient data loading and augmentation pipelines for large-scale training
  • Model quantization and pruning integration for further compression
  • Benchmarking suite comparing against baseline models and state-of-the-art methods

What I Learned

  • Knowledge distillation effectiveness varies significantly across different network architectures
  • Intermediate feature matching is crucial for preserving spatial understanding in segmentation
  • Balancing multiple distillation losses requires careful hyperparameter tuning
  • Student architecture design choices impact both accuracy and inference speed
  • Real-world deployment requires considering hardware-specific optimizations

Future Improvements

  • Neural architecture search for optimal student network design
  • Self-distillation techniques for further compression
  • Integration with model quantization and pruning for hybrid compression
  • Support for video segmentation and temporal consistency
  • Deployment optimization for specific hardware targets (mobile GPUs, NPUs)