object-detection
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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.
Evaluation Object Detection Metrics: IoU, AP, and the mAP Everyone Quotes
How detection is scored: IoU to decide a hit, greedy matching into TP/FP/FN, per-class Average Precision as the area under the precision–recall curve, and the COCO mAP@[.5:.95] you see in every paper, all on one running parking-lot example, with torchmetrics.
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.