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Object detection remains as one of the most notorious open problems incomputer vision. Despite large strides in accuracy in recent years, modernobject detectors have started to saturate on popular benchmarks raising thequestion of how far we can reach with deep learning tools and tricks. Here, byemploying 2 state-of-the-art object detection benchmarks, and analyzing morethan 15 models over 4 large scale datasets, we I) carefully determine theupperbound in AP, which is 91.6% on VOC (test2007), 78.2% on COCO (val2017),and 58.9% on OpenImages V4 (validation), regardless of the IOU. These numbersare much better than the mAP of the best model1 (47.9% on VOC, and 46.9% onCOCO; IOUs=.5:.95), II) characterize the sources of errors in object detectors,in a novel and intuitive way, and find that classification error (confusionwith other classes and misses) explains the largest fraction of errors andweighs more than localization and duplicate errors, and III) analyze theinvariance properties of models when surrounding context of an object isremoved, when an object is placed in an incongruent background, and when imagesare blurred or flipped vertically. We find that models generate boxes on emptyregions and that context is more important for detecting small objects thanlarger ones. Our work taps into the tight relationship between recognition anddetection and offers insights for building better models.

Empirical Upper Bound in Object Detection and More