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Evaluating the COCO mean average precision (MaP) and COCO recall metrics aspart of the static computation graph of modern deep learning frameworks poses aunique set of challenges. These challenges include the need for maintaining adynamic-sized state to compute mean average precision, reliance on globaldataset-level statistics to compute the metrics, and managing differing numbersof bounding boxes between images in a batch. As a consequence, it is commonpractice for researchers and practitioners to evaluate COCO metrics as a posttraining evaluation step. With a graph-friendly algorithm to compute COCO MeanAverage Precision and recall, these metrics could be evaluated at trainingtime, improving visibility into the evolution of the metrics through trainingcurve plots, and decreasing iteration time when prototyping new model versions.  Our contributions include an accurate approximation algorithm for MeanAverage Precision, an open source implementation of both COCO mean averageprecision and COCO recall, extensive numerical benchmarks to verify theaccuracy of our implementations, and an open-source training loop that includetrain-time evaluation of mean average precision and recall.

Efficient Graph-Friendly COCO Metric Computation for Train-Time Model Evaluation