Baaz Jhaj Studios

🎯 Project Overview

DistillKit is a comprehensive PyTorch implementation of knowledge distillation combined with model quantization for efficient deep learning. This project demonstrates how to train compact student models using knowledge from larger teacher models, then apply quantization techniques for deployment optimization.

Key Achievement: Complete pipeline for model compression achieving 85%+ accuracy retention with 90%+ size reduction.

🌟 Key Features

🧠
Knowledge Distillation with temperature scaling and soft/hard loss combination
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Mobile-Friendly Models using MobileNetV2 architecture optimized for CIFAR-10
⚡
Quantization Support with both static and quantization-aware training
📊
Comprehensive Evaluation including accuracy, latency, throughput, and model size
🔧
Modular Design with separate components for easy experimentation
📈
Performance Tracking with detailed metrics and visualization tools

🏗️ Technical Architecture

Pipeline Overview

1. Teacher Training: Train a large ResNet152 teacher model on CIFAR-10
2. Knowledge Distillation: Transfer knowledge from teacher to a smaller MobileNetV2 student
3. Model Quantization: Apply static and quantization-aware training (QAT) for deployment
4. Performance Evaluation: Compare accuracy, speed, and model size across all variants

Model Performance Comparison

Model	Accuracy	Size	Inference Time	Throughput
Teacher (ResNet152)	96.8%	230MB	45ms	22 FPS
Student (MobileNetV2)	94.2%	14MB	12ms	83 FPS
Quantized Student	93.8%	3.8MB	8ms	125 FPS

🔬 Technical Implementation

Knowledge Distillation Process

Temperature Scaling: Adjustable temperature parameter for soft target generation
Loss Combination: Weighted combination of hard and soft losses
Feature Alignment: Intermediate layer knowledge transfer
Training Strategy: Progressive distillation with learning rate scheduling

Quantization Techniques

Static Quantization: Post-training INT8 quantization
Quantization-Aware Training (QAT): Training with quantization simulation
Dynamic Quantization: Runtime quantization for inference
Custom Quantization: Layer-specific quantization strategies

📊 Results & Analysis

Compression Results

Size Reduction: 90%+ model size reduction (230MB → 3.8MB)
Speed Improvement: 5.6x faster inference
Accuracy Retention: 97% of original teacher accuracy maintained
Memory Efficiency: 85% reduction in memory footprint

Deployment Benefits

Edge Device Compatible: Optimized for mobile and embedded systems
Real-time Performance: Achieves real-time inference on resource-constrained devices
Energy Efficient: Reduced computational requirements for battery-powered devices
Scalable Architecture: Easy integration into larger ML pipelines

🚀 Applications & Use Cases

Computer Vision Applications

Image Classification: Efficient image recognition systems
Object Detection: Lightweight detection for mobile apps
Edge AI: Deployment on IoT and embedded devices
Real-time Processing: Video analysis with minimal latency

Educational Use

ML Research: Framework for distillation experiments
Academic Projects: Teaching tool for model compression
Benchmarking: Standardized evaluation of compression techniques
Prototyping: Rapid development of efficient models

DistillKit provides a comprehensive framework for researchers and practitioners to explore and implement state-of-the-art model compression techniques, making advanced AI accessible on resource-constrained devices.