VoVNet-v2 backbone networks in Detectron2

Efficient Backbone Network for Object Detection and Segmentation

[BibTeX]

In this project, we release code for VoVNet-v2 backbone network (introduced by CenterMask) in detectron2 as a extention form. VoVNet can extract diverse feature representation efficiently by using One-Shot Aggregation (OSA) module that concatenates subsequent layers at once. Since the OSA module can capture multi-scale receptive fields, the diversifed feature maps allow object detection and segmentation to address multi-scale objects and pixels well, especially robust on small objects. VoVNet-v2 improves VoVNet-v1 by adding identity mapping that eases the optimization problem and effective SE (Squeeze-and-Excitation) that enhances the diversified feature representation.

Highlight

Compared to ResNe(X)t backbone

• Efficient : Faster speed
• Accurate : Better performance, especially small object.

Update

• Lightweight-VoVNet-19 has been released. (19/02/2020)
• VoVNetV2-19-FPNLite has been released. (22/01/2020)
• centermask2 has been released. (20/02/2020)

Results on MS-COCO in Detectron2

Note

We measure the inference time of all models with batch size 1 on the same V100 GPU machine.
We train all models using V100 8GPUs.

• pytorch1.3.1
• CUDA 10.1
• cuDNN 7.3

Faster R-CNN

Lightweight-VoVNet with FPNLite

• DW and Slim denote depthwise separable convolution and a thiner model with half the channel size, respectively.

Mask R-CNN

Panoptic-FPN on COCO

Using this command with --num-gpus 1

python /path/to/vovnet-detectron2/train_net.py --config-file /path/to/vovnet-detectron2/configs/<config.yaml> --eval-only --num-gpus 1 MODEL.WEIGHTS <model.pth>

Installation

As this vovnet-detectron2 is implemented as a extension form (detectron2/projects) upon detectron2, you just install detectron2 following INSTALL.md.

Prepare for coco dataset following this instruction.

Training

ImageNet Pretrained Models

We provide backbone weights pretrained on ImageNet-1k dataset.

• VoVNetV2-19-Slim-DW
• VoVNetV2-19-Slim
• VoVNetV2-19-DW
• VoVNetV2-19
• VoVNetV2-39
• VoVNetV2-57
• VoVNetV2-99

To train a model, run

python /path/to/vovnet-detectron2/train_net.py --config-file /path/to/vovnet-detectron2/configs/<config.yaml>

For example, to launch end-to-end Faster R-CNN training with VoVNetV2-39 backbone on 8 GPUs, one should execute:

python /path/to/vovnet-detectron2/train_net.py --config-file /path/to/vovnet-detectron2/configs/faster_rcnn_V_39_FPN_3x.yaml --num-gpus 8

Evaluation

Model evaluation can be done similarly:

python /path/to/vovnet-detectron2/train_net.py --config-file /path/to/vovnet-detectron2/configs/faster_rcnn_V_39_FPN_3x.yaml --eval-only MODEL.WEIGHTS <model.pth>

TODO

• [x] Adding Lightweight models
• [ ] Applying VoVNet for other meta-architectures

Citing VoVNet

If you use VoVNet, please use the following BibTeX entry.

@inproceedings{lee2019energy,
  title = {An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection},
  author = {Lee, Youngwan and Hwang, Joong-won and Lee, Sangrok and Bae, Yuseok and Park, Jongyoul},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year = {2019}
}

@article{lee2019centermask,
  title={CenterMask: Real-Time Anchor-Free Instance Segmentation},
  author={Lee, Youngwan and Park, Jongyoul},
  booktitle={CVPR},
  year={2020}
}