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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.