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Evaluation Image Generation Metrics: FID, IS, KID, LPIPS, and CLIPScore
How do you score images with no ground truth? You compare distributions and use learned perceptual models: Inception Score, Fréchet Inception Distance, KID, LPIPS, and CLIPScore for prompt fidelity, on one running cat-generator example, with the equations, the catches, and torchmetrics.
Evaluation Multiclass and Multilabel Metrics: Micro, Macro, Weighted, and the Difference That Matters
How precision/recall/F1 scale past two classes: one-vs-rest, and the micro/macro/weighted averaging that decides whether rare classes count. Plus multilabel-specific metrics: subset accuracy, Hamming loss, and top-k, all on one running shelter-camera example, with scikit-learn.
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.
Evaluation Object Tracking Metrics: MOTA, MOTP, IDF1, and HOTA
Tracking adds identity over time, so its metrics score two things at once: detecting objects and keeping their IDs consistent. MOTA, MOTP, ID switches, IDF1, and the modern HOTA that balances detection against association, on one running door-cam example, with the equations and motmetrics.
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.
Evaluation Segmentation Metrics: IoU, Dice, mIoU, and Panoptic Quality
Scoring masks instead of boxes: pixel IoU/Jaccard and the Dice coefficient (and why they're related), mIoU for semantic segmentation, mask-AP for instance segmentation, and Panoptic Quality for the unified task, on one running cat-mask example, with the equations and code.
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.