Object Detection
Finding where objects are in an image and what they are — drawing a labelled box around each one.
When not to use it
- When one label for the whole image is enough. Detection costs more to label, train, run, and evaluate. Don't buy it if you don't need boxes.
- When you need exact shape. A box around a curved or overlapping object is a crude approximation — segmentation is the right tool.
- On tiny, dense, or heavily overlapping objects without a model specifically chosen for it. Generic detectors degrade badly there.
Reach for something else instead
- Classification for single-subject images.
- Segmentation when boundaries matter more than boxes.
- Classical computer vision — thresholding, template matching, edge detection — is still unbeaten for controlled environments like a factory line with fixed lighting.
Sources & further reading
- Girshick et al. (2013), Rich feature hierarchies (R-CNN) and Ren et al. (2015), Faster R-CNN — the two-stage lineage.
- Redmon et al. (2015), You Only Look Once — YOLO, and the real-time trade-off.
- Lin et al. (2014), Microsoft COCO — the dataset whose mAP metric everyone quotes and few define.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Reporting mAP without saying at what IoU threshold, which makes the number meaningless to anyone else.
- Ignoring non-maximum suppression settings, then wondering about duplicate boxes.
- Labelling inconsistently. Two annotators who disagree about where the box ends will cap your model's accuracy below their agreement rate.
At a glance
FieldComputer Vision
Core idealocate and label each object
Key trade-offspeed vs. accuracy
Metricmean Average Precision (mAP)
DifficultyIntermediate
Flashcards for this concept
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