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

2. Install MXNet following https://mxnet.incubator.apache.org/get_started. We tested our code on MXNet v1.3.0.

3. 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

3. 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/.

4. To test your trained model

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

Other implementations

Pytorch: MMTracking

Acknowledge

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