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

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: First gradually, then suddenly: Understanding the impact of image compression on object detection using deep learning
 
Identifier https://doi.org/10.7910/DVN/UPIKSF
 
Creator Gandor, Tomasz
Nalepa, Jakub
 
Publisher Harvard Dataverse
 
Description

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_<model_name>_<quality>, with results for a particular compression quality for that model. Each folder contains the following files:



  1. results.json.gz - summarized performance metrics, overall and per-class

  2. 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.


 
Subject Computer and Information Science
jpeg compression
object detection
deep learning
 
Contributor Gandor, Tomasz
 
Type process-produced data