Edge Object Detection: Model Architecture and Compression

Deploying powerful computer vision models on low-power edge devices requires balancing high accuracy with strict hardware limitations. This text-based course guides you through the core principles of selecting, designing, and optimizing object detection architectures for real-world edge hardware. You will transition from understanding basic neural networks to confidently compressing and preparing advanced models for real-time deployment, learning how to reduce model size and latency while maintaining safety and performance requirements. What you'll learn: - Understand the foundational architectures of key object detection models, including YOLO, EfficientDet, and DETR. - Apply model compression techniques such as pruning, weight sharing, and post-training quantization. - Implement Quantization-Aware Training (QAT) to preserve model accuracy during hardware conversion. - Evaluate latency, throughput, and power consumption trade-offs on resource-constrained edge systems. - Configure optimized models for deployment on modern edge runtime engines. The course begins with essential terminology and foundational concepts of computer vision architectures before guiding you through practical compression strategies and edge-specific deployment workflows. This program is designed for aspiring AI engineers and developers looking to transition into edge computing, with no advanced hardware prerequisites required. Start reading today to master the fundamentals of efficient edge AI deployment.