edge-ai
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Edge Deployment Build a computer-vision plugin for OBS Studio, and measure what every frame costs
A step-by-step OBS Studio video filter in C++ on OpenVINO, split so the model swaps without touching the plumbing, and measured stage by stage: getting pixels off the GPU, where inference has to live so OBS never drops a frame, and what the trip from camera to virtual camera costs.
Models & Detection YOLO-NAS Strikes Back: Still the Fastest Detector on Hardware You Already Own
Quantized to INT8, YOLO-NAS-S runs at 270 FPS on a laptop CPU and 1146 FPS on a mid-range laptop GPU, and it drops straight into Frigate. The model aged well; its tooling did not. Here is a clean reimplementation, measured across CPU, Intel iGPU and NVIDIA dGPU, and checked bit-for-bit against the original.
Edge Deployment RF-DETR vs YOLO-NAS: A Practical Benchmark for Edge Deployment: CPU, GPU, and Intel iGPU Compared
RF-DETR Nano vs YOLO-NAS-S on COCO across CPU, CUDA GPU, and Intel Iris Xe iGPU, across two resolutions, OpenVINO FP32/FP16/INT8, and a custom fine-tune, all under one consistent evaluation. RF-DETR wins accuracy; YOLO-NAS wins latency, efficiency, and INT8.
Models & Detection CNN vs Transformer, quantized: YOLO26-seg and RF-DETR-Seg race for instance masks on an Intel iGPU
Two opposite small segmentation models, a 2026-era CNN and a DETR transformer, both quantized to INT8 with NNCF and run on a laptop Intel iGPU. YOLO26-seg's forward pass is ~7.4× faster; RF-DETR-Seg keeps its masks more faithful under INT8 on the CPU, and its fully-quantized mask head breaks on the iGPU. The whole shootout, numbers first.