Skip to content

MahirShah202011002/Sequence-Level-Semantics-Aggregation

 
 

Repository files navigation

Sequence Level Semantics Aggregation for Video Object Detection

Introduction

This is an official MXNet implementation of Sequence Level Semantics Aggregation for Video Object Detection. (ICCV 2019, oral). SELSA aggregates full-sequence level information of videos while keeping a simple and clean pipeline. It achieves 82.69 mAP with ResNet-101 on ImageNet VID validation set.

Citation

If you use the code or models in your research, please cite with:

@article{wu2019selsa,
  title={Sequence Level Semantics Aggregation for Video Object Detection},
  author={Wu, Haiping and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang},
  journal={ICCV 2019},
  year={2019}
}

Main Results

training data testing data mAP(%) mAP(%)
(slow)
mAP(%)
(medium)
mAP(%)
(fast)
Single-frame baseline
(Faster R-CNN, ResNet-101)
ImageNet DET train
+ VID train
ImageNet VID validation 73.6 82.1 71.0 52.5
SELSA
(Faster R-CNN, ResNet-101)
ImageNet DET train
+ VID train
ImageNet VID validation 80.3 86.9 78.9 61.4
SELSA
(Faster R-CNN, ResNet-101, Data Aug)
ImageNet DET train
+ VID train
ImageNet VID validation 82.7 88.0 81.4 67.1

Installation

Please note that this repo is based on Python 2.

  1. Clone the repository.
git clone https://github.com/happywu/Sequence-Level-Semantics-Aggregation
  1. Install MXNet following https://mxnet.incubator.apache.org/get_started. We tested our code on MXNet v1.3.0.

  2. Install packages via

pip install -r requirements.txt
sh init.sh

Preparation for Training & Testing

  1. Please download ILSVRC2015 DET and ILSVRC2015 VID dataset, and make sure it looks like this:

    ./data/ILSVRC2015/
    ./data/ILSVRC2015/Annotations/DET
    ./data/ILSVRC2015/Annotations/VID
    ./data/ILSVRC2015/Data/DET
    ./data/ILSVRC2015/Data/VID
    ./data/ILSVRC2015/ImageSets
    
  2. Please download ImageNet pre-trained ResNet-v1-101 model and our pretrained SELSA ResNet-101 model manually, and put it under folder ./model. Make sure it looks like this:

    ./model/pretrained_model/resnet_v1_101-0000.params
    ./model/pretrained_model/selsa_rcnn_vid-0000.params
    

Testing

  1. To test the provided pretrained model, run the following command.
    python experiments/selsa/test.py --cfg experiments/selsa/cfgs/resnet_v1_101_rcnn_selsa_aug.yaml --test-pretrained ./model/pretrained_model/selsa_rcnn_vid
    

You should get the results as reported before.

Training

  1. To train, use the following command

    python experiments/selsa/train_end2end.py --cfg experiments/selsa/cfgs/resnet_v1_101_rcnn_selsa_aug.yaml
    

    A cache folder would be created automatically to save the model and the log under output/selsa_rcnn/imagenet_vid/.

  2. To test your trained model

    python experiments/selsa/test.py --cfg experiments/selsa/cfgs/resnet_v1_101_rcnn_selsa_aug.yaml
    

Acknowledge

This repo is modified from Flow-Guided-Feature-Aggregation.

About

Sequence Level Semantics Aggregation for Video Object Detection

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 59.3%
  • Cuda 35.7%
  • C++ 5.0%