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Handbook · 6 chapters

The Computer Vision Metrics Handbook

Every score in computer vision, from the ground up. One confusion matrix grows into precision and recall; those scale to many classes, stretch over boxes and masks, follow identities through time, and finally measure images with no ground truth at all. Six chapters — the equations, the intuition, and runnable code.

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Classification → Multiclass & multilabel → Object detection → Segmentation → Object tracking → Image generation
  1. 01
    Classification

    Classification Metrics, From the Ground Up: Precision, Recall, F1, ROC and AUC

    The confusion matrix and everything that falls out of it — precision, recall, F1, specificity, the precision/recall trade-off, PR and ROC curves, and AUC — built on one running toy example (a backyard cat-cam), with the equations, the intuition, and scikit-learn.

    Confusion matrixPrecisionRecallF1SpecificityPR & ROC curvesAUC
  2. 02
    Multiclass & multilabel

    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.

    One-vs-restMicroMacroWeightedSubset accuracyHamming lossTop-k
  3. 03
    Object detection

    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.

    IoUAPPR curvemAP
  4. 04
    Segmentation

    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.

    IoU / JaccardDicemIoUMask APPanoptic Quality
  5. 05
    Object tracking

    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.

    MOTAMOTPID switchesIDF1HOTA
  6. 06
    Image generation

    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.

    Inception ScoreFIDKIDLPIPSCLIPScore

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