Towards Robust Adaptive Object Detection under Noisy Annotations

Code for CVPR-22 paper "Towards Robust Adaptive Object Detection under Noisy Annotations"

1. Introduction

Existing methods assume that the source domain labels are completely clean, yet large-scale datasets often contain error-prone annotations due to instance ambiguity, which may lead to a biased source distribution and severely degrade the performance of the domain adaptive detector de facto.

2. Data Preparation

Prepare VOC data set following py-faster-rcnn

Prepare Clipart/Watercolor data set following cross-domain-detection

Prepare Cityscapes/Foggy Cityscapes data set following Everypixelmatters

Change the directory in ./maskrcnn_benchmark/config/path_catalog.py correspondingly.

3. Installation (following maskrcnn-benchmark)

Requirements:

• PyTorch 1.0 from a nightly release. Installation instructions can be found in https://pytorch.org/get-started/locally/
• torchvision from master
• cocoapi
• yacs
• matplotlib
• GCC >= 4.9
• (optional) OpenCV for the webcam demo

Option 1: Step-by-step installation

# first, make sure that your conda is setup properly with the right environment
# for that, check that `which conda`, `which pip` and `which python` points to the
# right path. From a clean conda env, this is what you need to do

conda create --name nlte
source activate nlte

# this installs the right pip and dependencies for the fresh python
conda install ipython

# maskrcnn_benchmark and coco api dependencies
pip install ninja yacs cython matplotlib

# follow PyTorch installation in https://pytorch.org/get-started/locally/
# we give the instructions for CUDA 9.0
conda install pytorch-nightly -c pytorch

# install torchvision
cd ~/github
git clone https://github.com/pytorch/vision.git
cd vision
python setup.py install

# install pycocotools
cd ~/github
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install

# install PyTorch Detection
cd ~/github
git clone https://github.com/CityU-AIM-Group/NLTE.git
cd NLTE
# the following will install the lib with
# symbolic links, so that you can modify
# the files if you want and won't need to
# re-build it
python setup.py build develop

# or if you are on macOS
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develop

4. Generate Symmetric Noise for Source domain (VOC)

Put make_sym_noise.py into VOCdevkit, the structure should be like:

VOCdevkit
└─ VOC2007
   └─ Annotations
   └─ ImageSets
   └─ JPEGImages
└─ VOC2012
   └─ Annotations
   └─ ImageSets
   └─ JPEGImages
└─ make_sym_noise.py

run

python make_sym_noise.py

5. Train/test The Model

Train:

python -m torch.distributed.launch --nproc_per_node=$NGPUS ./tools/train_net.py --config-file "path/to/config/file.yaml" DATASETS.ANNOTATIONS Noisy_Annotations_per_$NOISYRATE

Test:

python ./tools/train_net.py --config-file "path/to/config/file.yaml" MODEL.WEIGHT $YOURWEIGHT SOLVER.TEST_ONLY True

The trained models are provided in Google Drive

6. Citation

If you found this work useful for your research, please cite our paper:

@inproceedings{liu2022towards,
  title={Towards Robust Adaptive Object Detection under Noisy Annotations},
  author={Liu, Xinyu and Li, Wuyang and Yang, Qiushi and Li, Baopu and Yuan, Yixuan},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

7. Acknowledgement

This work is based on Domain-Adaptive-Faster-RCNN and maskrcnn-benchmark.