# A Parallel Implementation of Computing Mean Average Precision

arXiv: https://arxiv.org/abs/2206.09504 · HTML: https://mlsanity.com/paper/2206.09504/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 19 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [End-to-end training of object class detectors for mean average precision](https://mlsanity.com/paper/1607.03476/) (2016)

## Built on by (most cited first, 4 of 4)

- [NBBOX: Noisy Bounding Box Improves Remote Sensing Object Detection](https://mlsanity.com/paper/2409.09424/) (2024)
- [A comprehensive GeoAI review: Progress, Challenges and Outlooks](https://mlsanity.com/paper/2412.11643/) (2024)
- [Investigating Long-term Training for Remote Sensing Object Detection](https://mlsanity.com/paper/2407.15143/) (2024)
- [AI and Vision based Autonomous Navigation of Nano-Drones in Partially-Known Environments](https://mlsanity.com/paper/2505.04972/) (2025)

## Abstract (arXiv)

Mean Average Precision (mAP) has been widely used for evaluating the quality of object detectors, but an efficient implementation is still absent. Current implementations can only count true positives (TP's) and false positives (FP's) for one class at a time by looping through every detection of that class sequentially. Not only are these approaches inefficient, but they are also inconvenient for reporting validation mAP during training. We propose a parallelized alternative that can process mini-batches of detected bounding boxes (DTBB's) and ground truth bounding boxes (GTBB's) as inference goes such that mAP can be instantly calculated after inference is finished. Loops and control statements in sequential implementations are replaced with extensive uses of broadcasting, masking, and indexing. All operators involved are supported by popular machine learning frameworks such as PyTorch and TensorFlow. As a result, our implementation is much faster and can easily fit into typical training routines. A PyTorch version of our implementation is available at https://github.com/bwangca/fast-map.