DOI: 10.7910/dvn/uhep3c
title: Replication Data for: Training Deep Convolutional Object Detectors for Images Affected by Lossy Compression

description: &lt;p&gt;This collection contains the trained models and object detection
results of 2 architectures found in the Detectron2 library, on the MS
COCO val2017 dataset, under different JPEG compresion level Q = {5, 12,
19, 26, 33, 40, 47, 54, 61, 68, 75, 82, 89, 96} (14 levels per trained
model).&lt;/p&gt;
&lt;p&gt;Architectures:&lt;br /&gt;
F50 – Faster R-CNN on ResNet-50 with FPN&lt;br /&gt;
R50 – RetinaNet on ResNet-50 with FPN&lt;/p&gt;
&lt;p&gt;Training type:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;D2 – Detectron2 Model ZOO pre-trained 1x model (90.000 iterations,
batch 16)&lt;/li&gt;
&lt;li&gt;STD – standard 1x training (90.000 iterations) on original train2017
dataset&lt;/li&gt;
&lt;li&gt;Q20 – 1x training (90.000 iterations) on train2017 dataset degraded
to Q=20&lt;/li&gt;
&lt;li&gt;Q40 – 1x training (90.000 iterations) on train2017 dataset degraded
to Q=40&lt;/li&gt;
&lt;li&gt;T20 – extra 1x training on top of D2 on train2017 dataset degraded
to Q=20&lt;/li&gt;
&lt;li&gt;T40 – extra 1x training on top of D2 on train2017 dataset degraded
to Q=40&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="model-and-metrics-files"&gt;Model and metrics files&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;&lt;code&gt;models_FasterRCNN.tar.gz&lt;/code&gt; (F50-STD, F50-Q20, …)&lt;/li&gt;
&lt;li&gt;&lt;code&gt;models_RetinaNet.tar.gz&lt;/code&gt; (R50-STD, R50-Q20, …)&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;For every model there are 3 files:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;code&gt;config.yaml&lt;/code&gt; – the Detectron2 config of the model.&lt;/li&gt;
&lt;li&gt;&lt;code&gt;model_final.pth&lt;/code&gt; – the weights (training snapshot) in
PyTorch format.&lt;/li&gt;
&lt;li&gt;&lt;code&gt;metrics.json&lt;/code&gt; – training metrics (like time, total loss,
etc.) every 20 iterations.&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;The D2 models were not included, because they are available from the
&lt;a
href="https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md"&gt;Detectron2
Model ZOO&lt;/a&gt;, as &lt;code&gt;faster_rcnn_R_50_FPN_1x&lt;/code&gt; (F50-D2) and
&lt;code&gt;retinanet_R_50_FPN_1x&lt;/code&gt; (R50-D2).&lt;/p&gt;
&lt;h2 id="result-files"&gt;Result files&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;&lt;code&gt;F50-results.tar.gz&lt;/code&gt; – results for Faster R-CNN models
(inluding D2).&lt;/li&gt;
&lt;li&gt;&lt;code&gt;R50-results.tar.gz&lt;/code&gt; – results for RetinaNet models
(inluding D2).&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;For every model there are 14 subdirectories,
e.g. &lt;code&gt;evaluator_dump_R50x1_005&lt;/code&gt; through
&lt;code&gt;evaluator_dump_R50x1_096&lt;/code&gt;, for each of the JPEG Q values.
Each such folder contains:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;code&gt;coco_instances_results.json&lt;/code&gt; – all detected objects
(image id, bounding box, class index and confidence).&lt;/li&gt;
&lt;li&gt;&lt;code&gt;results.json&lt;/code&gt; – AP metrics as computed by COCO API.&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="source-code-for-processing-the-data"&gt;Source code for processing
the data&lt;/h2&gt;
&lt;p&gt;The data can be processed using our code, published at: &lt;a
href="https://github.com/tgandor/urban_oculus"
class="uri"&gt;https://github.com/tgandor/urban_oculus&lt;/a&gt;.&lt;/p&gt;
&lt;h3 id="additional-dependencies-for-the-source-code"&gt;Additional
dependencies for the source code:&lt;/h3&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/cocodataset/cocoapi"&gt;COCO API&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a
href="https://github.com/facebookresearch/detectron2"&gt;Detectron2&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;