Replication Data for: First gradually, then suddenly: Understanding the impact of image compression on object detection using deep learning

This collection contains the object detection results of 9 architectures found in the Detectron2 library, for the MS COCO val2017 dataset, under different compresion level Q = 1, 2, …, 100. The stored results include all detections above 0.5 confidence score threshold, and allows for re-calculation of the performance metrics. There are 9 per-model archive files, and each file contains 100 subfolders named evaluator_dump__, with results for a particular compression quality for that model. Each folder contains the following files: results.json.gz - summarized performance metrics, overall and per-class coco_instances_results.json.gz - detailed results for each image, with object classes and bounding boxes. The last file, baseline_05.tar.gz contains 9 folders, per model, with the same structure as above, only obtained using the original image quality. Supplementary data: counts_vs_Tc_by_Q.pdf – a PDF with multiple plots of object counts (TP, FP, EX), for every compression quality Q. PRF1_vs_Tc_by_Q.pdf – a PDF with multiple plots of Precision, Recall and F1-score (PPV, TPR, F1), for every compression quality Q. rate_ssim_byQ.tar.gz – archive with JSON files containing image information (quality metrics) for every quality, for every image in COCO val2017.