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# Corruption Benchmarking
## Introduction
We provide tools to test object detection and instance segmentation models on the image corruption benchmark defined in [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484).
This page provides basic tutorials how to use the benchmark.
```latex
@article{michaelis2019winter,
title={Benchmarking Robustness in Object Detection:
Autonomous Driving when Winter is Coming},
author={Michaelis, Claudio and Mitzkus, Benjamin and
Geirhos, Robert and Rusak, Evgenia and
Bringmann, Oliver and Ecker, Alexander S. and
Bethge, Matthias and Brendel, Wieland},
journal={arXiv:1907.07484},
year={2019}
}
```
## About the benchmark
To submit results to the benchmark please visit the [benchmark homepage](https://github.com/bethgelab/robust-detection-benchmark)
The benchmark is modelled after the [imagenet-c benchmark](https://github.com/hendrycks/robustness) which was originally
published in [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261) (ICLR 2019) by Dan Hendrycks and Thomas Dietterich.
The image corruption functions are included in this library but can be installed separately using:
```shell
pip install imagecorruptions
```
Compared to imagenet-c a few changes had to be made to handle images of arbitrary size and greyscale images.
We also modfied the 'motion blur' and 'snow' corruptions to remove dependency from a linux specific library,
which would have to be installed separately otherwise. For details please refer to the [imagecorruptions repository](https://github.com/bethgelab/imagecorruptions).
## Inference with pretrained models
We provide a testing script to evaluate a models performance on any combination of the corruptions provided in the benchmark.
### Test a dataset
- [x] single GPU testing
- [ ] multiple GPU testing
- [ ] visualize detection results
You can use the following commands to test a models performance under the 15 corruptions used in the benchmark.
```shell
# single-gpu testing
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
```
Alternatively different group of corruptions can be selected.
```shell
# noise
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions noise
# blur
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions blur
# wetaher
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions weather
# digital
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions digital
```
Or a costom set of corruptions e.g.:
```shell
# gaussian noise, zoom blur and snow
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions gaussian_noise zoom_blur snow
```
Finally the corruption severities to evaluate can be chosen.
Severity 0 corresponds to clean data and the effect increases from 1 to 5.
```shell
# severity 1
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 1
# severities 0,2,4
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 0 2 4
```
## Results for modelzoo models
The results on COCO 2017val are shown in the below table.
Model | Backbone | Style | Lr schd | box AP clean | box AP corr. | box % | mask AP clean | mask AP corr. | mask % |
:-----:|:---------:|:-------:|:-------:|:------------:|:------------:|:-----:|:-------------:|:-------------:|:------:|
Faster R-CNN | R-50-FPN | pytorch | 1x | 36.3 | 18.2 | 50.2 | - | - | - |
Faster R-CNN | R-101-FPN | pytorch | 1x | 38.5 | 20.9 | 54.2 | - | - | - |
Faster R-CNN | X-101-32x4d-FPN | pytorch |1x | 40.1 | 22.3 | 55.5 | - | - | - |
Faster R-CNN | X-101-64x4d-FPN | pytorch |1x | 41.3 | 23.4 | 56.6 | - | - | - |
Faster R-CNN | R-50-FPN-DCN | pytorch | 1x | 40.0 | 22.4 | 56.1 | - | - | - |
Faster R-CNN | X-101-32x4d-FPN-DCN | pytorch | 1x | 43.4 | 26.7 | 61.6 | - | - | - |
Mask R-CNN | R-50-FPN | pytorch | 1x | 37.3 | 18.7 | 50.1 | 34.2 | 16.8 | 49.1 |
Mask R-CNN | R-50-FPN-DCN | pytorch | 1x | 41.1 | 23.3 | 56.7 | 37.2 | 20.7 | 55.7 |
Cascade R-CNN | R-50-FPN | pytorch | 1x | 40.4 | 20.1 | 49.7 | - | - | - |
Cascade Mask R-CNN | R-50-FPN | pytorch | 1x| 41.2 | 20.7 | 50.2 | 35.7 | 17.6 | 49.3 |
RetinaNet | R-50-FPN | pytorch | 1x | 35.6 | 17.8 | 50.1 | - | - | - |
Hybrid Task Cascade | X-101-64x4d-FPN-DCN | pytorch | 1x | 50.6 | 32.7 | 64.7 | 43.8 | 28.1 | 64.0 |
Results may vary slightly due to the stochastic application of the corruptions.
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