Editing Computer Vision (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert CV practitioners use a layered evaluation strategy:

'''mAP (mean Average Precision)''': The standard metric for object detection. Computed as the mean of per-class average precision at IoU thresholds (0.5, 0.5:0.95). mAP@0.5:0.95 is the COCO benchmark standard.

'''Confusion matrix analysis''': Which classes are being confused with each other? Is the model confusing "pedestrian" with "cyclist" at distances? This reveals actionable improvements.

'''Calibration of confidence scores''': Object detectors output confidence scores. A well-calibrated detector's 0.9 confidence should be right ~90% of the time. Use temperature scaling post-hoc.

'''Latency profiling''': FPS (frames per second) on the target hardware. A 95% accurate model running at 5 FPS on a drone is less useful than a 90% accurate model running at 30 FPS. Use TensorRT, ONNX, or CoreML for hardware-specific optimization.

Expert practitioners also perform '''sliced evaluation''' — measuring performance separately across subgroups (day/night, near/far objects, occluded/visible) to surface hidden disparities between overall and subgroup accuracy.
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