# A Mask-RCNN Baseline for Probabilistic Object Detection

Source: [A Mask-RCNN Baseline for Probabilistic Object Detection](<https://www.sota2.com/research/paper/68fa66811e209131cea10be0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:52:21.192Z

Authors: Phil Ammirato, Alexander C. Berg

Published: 2019-08-09T20:45:46.000Z

## Abstract

The Probabilistic Object Detection Challenge evaluates object detection methods using a new evaluation measure, Probability-based Detection Quality (PDQ), on a new synthetic image dataset. We present our submission to the challenge, a fine-tuned version of Mask-RCNN with some additional post-processing. Our method, submitted under username pammirato, is currently second on the leaderboard with a score of 21.432, while also achieving the highest spatial quality and average overall quality of detections. We hope this method can provide some insight into how detectors designed for mean average precision (mAP) evaluation behave under PDQ, as well as a strong baseline for future work.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1908.03621>)

[PDF](<https://arxiv.org/pdf/1908.03621.pdf>)