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Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training, ECCV 2020

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Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training

By Hongkai Zhang, Hong Chang, Bingpeng Ma, Naiyan Wang, Xilin Chen.

This project is based on maskrcnn-benchmark.

[2020.7] Dynamic R-CNN is officially included in MMDetection V2.2, many thanks to @xvjiarui and @hellock for migrating the code.


Although two-stage object detectors have continuously advanced the state-of-the-art performance in recent years, the training process itself is far from crystal. In this work, we first point out the inconsistency problem between the fixed network settings and the dynamic training procedure, which greatly affects the performance. For example, the fixed label assignment strategy and regression loss function cannot fit the distribution change of proposals and are harmful to training high quality detectors. Then, we propose Dynamic R-CNN to adjust the label assignment criteria (IoU threshold) and the shape of regression loss function (parameters of SmoothL1 Loss) automatically based on the statistics of proposals during training. This dynamic design makes better use of the training samples and pushes the detector to fit more high quality samples. Specifically, our method improves upon ResNet-50-FPN baseline with 1.9% AP and 5.5% AP90 on the MS COCO dataset with no extra overhead. For more details, please refer to our paper.



Multi-scale training AP (minival) AP (test-dev) Trained model
DynamicRCNNr50fpn1x No 38.9 39.1 Google Drive
DynamicRCNNr50fpn2x No 39.9 39.9 Google Drive
DynamicRCNNr101fpn1x No 41.0 41.2 Google Drive
DynamicRCNNr101fpn2x No 41.8 42.0 Google Drive
DynamicRCNNr101fpn3x Yes 44.4 44.7 Google Drive
DynamicRCNNr101dcnv2fpn_3x Yes 46.7 46.9 Google Drive

  1. 1x
    mean the model is trained for 90K, 180K and 270K iterations, respectively.
  2. For
    Multi-scale training
    , the shorter side of images is randomly chosen from (400, 600, 800, 1000, 1200), and the longer side is 1400. We also extend the training time by
    under this setting.
  3. dcnv2
    denotes deformable convolutional networks v2. We follow the same setting as maskrcnn-benchmark. Note that the result of this version is slightly lower than that of mmdetection.
  4. All results in the table are obtained using a single model with no extra testing tricks. Additionally, adopting multi-scale testing on model
    achieves 49.2% in AP on COCO test-dev. Please set
    in the
    to enable multi-scale testing. Here we use five scales with shorter sides (800, 1000, 1200, 1400, 1600) and the longer side is 2000 pixels. Note that Dynamic R-CNN*(50.1% AP) in Table 9 is implemented using MMDetection v1.1, please refer to this link.
  5. If you want to test the model provided by us, please refer to Testing.

Getting started


0. Requirements

  • pytorch (v1.0.1.post2, other version have not been tested)
  • torchvision (v0.2.2.post3, other version have not been tested)
  • cocoapi
  • matplotlib
  • tqdm
  • cython
  • easydict
  • opencv

Anaconda3 is recommended here since it integrates many useful packages. Please make sure that your conda is setup properly with the right environment. Then install

manually as follows:
pip install torch==1.0.1.post2
pip install torchvision==0.2.2.post3

Other dependencies will be installed during


1. Clone this repo

git clone

2. Compile kernels

Please make sure your

is successfully installed and be added to the
. I only test
for my experiments.
cd ${DynamicRCNN_ROOT}
python build develop

3. Prepare data and output directory

cd ${DynamicRCNN_ROOT}
mkdir data
mkdir output

Prepare data and pretrained models: - COCO dataset - ImageNet Pretrained Models from Detectron

Then organize them as follows:

├── dynamic_rcnn
├── models
├── output
├── data
│   ├── basemodels/R-50.pkl
│   ├── coco
│   │   ├── annotations
│   │   ├── train2017(2014)
│   │   ├── val2017(2014)


We use

in order to launch multi-gpu training.
cd models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r50_fpn_1x
python -m torch.distributed.launch --nproc_per_node=8


Training and testing logs will be saved automatically in the

directory following the same path as in

For example, the experiment directory and log directory are formed as follows:


And you can link the

to your experiment directory by this script in the experiment directory:
python -log



to specify iteration for testing, default is the latest model.
# for regular testing and evaluation
python -m torch.distributed.launch --nproc_per_node=8
# for specified iteration
python -m torch.distributed.launch --nproc_per_node=8 -i $iteration_number

If you want to test our provided model, just download the model, move it to the corresponding log directory and create a symbolic link like follows:

# example for Dynamic_RCNN_r50_fpn_1x
cd models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r50_fpn_1x
python -log
realpath log | xargs mkdir
mkdir -p log/checkpoints
mv path/to/the/model log/checkpoints
realpath log/checkpoints/dynamic_rcnn_r50_fpn_1x_test_model_0090000.pth last_checkpoint | xargs ln -s

Then you can follow the regular testing and evaluation process.

Third-party resources



Please consider citing our paper in your publications if it helps your research:

    author = {Zhang, Hongkai and Chang, Hong and Ma, Bingpeng and Wang, Naiyan and Chen, Xilin},
    title = {Dynamic {R-CNN}: Towards High Quality Object Detection via Dynamic Training},
    booktitle = {ECCV},
    year = {2020}

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