Comprehensive implementation guide for training and quantizing YOLOv8 models for edge deployment. Covers PTQ and QAT workflows, model export to ONNX/TensorRT/TFLite formats, rigorous validation methodologies, and performance benchmarking demonstrating 4x compression and 1.5-2.75x speedup with sub-2% accuracy degradation.
Tag: quantization
Real-Time Object Detection on Edge Devices: Building Production-Ready CNNs for On-Device Visual Analysis
Comprehensive guide to deploying production-ready CNNs on edge devices for real-time object detection. Covers architecture fundamentals, YOLOv8 vs YOLO26 comparison, quantization techniques achieving 4x compression, and hardware platform selection including NVIDIA Jetson, Raspberry Pi + Coral TPU, and Intel OpenVINO solutions.
Vector Databases: From Hype to Production Reality – Part 2: Architecture and Indexing Algorithms
In Part 1, we explored what vector databases are and why they have become essential infrastructure for AI applications. Now we dive into the technical
