ICAR Research Data Repository for Knowledge Management

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).

Architectures:

F50 – Faster R-CNN on ResNet-50 with FPN

R50 – RetinaNet on ResNet-50 with FPN

Training type:

• D2 – Detectron2 Model ZOO pre-trained 1x model (90.000 iterations,
  batch 16)


• STD – standard 1x training (90.000 iterations) on original train2017
  dataset


• Q20 – 1x training (90.000 iterations) on train2017 dataset degraded
  to Q=20


• Q40 – 1x training (90.000 iterations) on train2017 dataset degraded
  to Q=40


• T20 – extra 1x training on top of D2 on train2017 dataset degraded
  to Q=20


• T40 – extra 1x training on top of D2 on train2017 dataset degraded
  to Q=40

• models_FasterRCNN.tar.gz (F50-STD, F50-Q20, …)


• models_RetinaNet.tar.gz (R50-STD, R50-Q20, …)

For every model there are 3 files:

• config.yaml – the Detectron2 config of the model.


• model_final.pth – the weights (training snapshot) in
  PyTorch format.


• metrics.json – training metrics (like time, total loss,
  etc.) every 20 iterations.

The D2 models were not included, because they are available from the
Detectron2
Model ZOO, as faster_rcnn_R_50_FPN_1x (F50-D2) and
retinanet_R_50_FPN_1x (R50-D2).

• F50-results.tar.gz – results for Faster R-CNN models
  (inluding D2).


• R50-results.tar.gz – results for RetinaNet models
  (inluding D2).

For every model there are 14 subdirectories,
e.g. evaluator_dump_R50x1_005 through
evaluator_dump_R50x1_096, for each of the JPEG Q values.
Each such folder contains:

• coco_instances_results.json – all detected objects
  (image id, bounding box, class index and confidence).


• results.json – AP metrics as computed by COCO API.

The data can be processed using our code, published at: https://github.com/tgandor/urban_oculus.

• COCO API


• Detectron2