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Memory Enhanced Global-Local Aggregation for Video Object Detection, CVPR2020


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MEGA for Video Object Detection

License PWC

By Yihong Chen, Yue Cao, Han Hu, Liwei Wang.

This repo is an official implementation of "Memory Enhanced Global-Local Aggregation for Video Object Detection", accepted by CVPR 2020. This repository contains a PyTorch implementation of our approach MEGA based on maskrcnn_benchmark, as well as some training scripts to reproduce the results on ImageNet VID reported in our paper.

Besides, this repository also implements several other algorithms like FGFA and RDN. Any new methods are welcomed. Hoping for your pull request! We hope this repository would help further research in the field of video object detection and beyond. :)

Citing MEGA

Please cite our paper in your publications if it helps your research:

    Author = {Chen, Yihong and Cao, Yue and Hu, Han and Wang, Liwei},
    Title = {Memory Enhanced Global-Local Aggregation for Video Object Detection},
    Conference = {CVPR},
    Year = {2020}


  • Add motion-IoU specific AP evaluation code. Only available for ImageNet VID dataset. (19/06/2020)
  • Demo for visualization added (Support image folder and video). (17/06/2020)
  • Results of ResNet-50 backbone added. (13/04/2020)
  • Code and pretrained weights for Deep Feature Flow released. (30/03/2020)

Main Results

Pretrained models are now available at Baidu (code: neck) and Google Drive.

Model Backbone AP50 AP (fast) AP (med) AP (slow) Link
single frame baseline ResNet-101 76.7 52.3 74.1 84.9 Google
DFF ResNet-101 75.0 48.3 73.5 84.5 Google
FGFA ResNet-101 78.0 55.3 76.9 85.6 Google
RDN-base ResNet-101 81.1 60.2 79.4 87.7 Google
RDN ResNet-101 81.7 59.5 80.0 89.0 Google
MEGA ResNet-101 82.9 62.7 81.6 89.4 Google
Model Backbone AP50 AP (fast) AP (med) AP (slow) Link
single frame baseline ResNet-50 71.8 47.2 69.2 80.6 Google
DFF ResNet-50 70.4 43.6 68.9 80.8 Google
FGFA ResNet-50 74.3 50.6 72.3 84.0 Google
RDN-base ResNet-50 76.7 53.8 74.8 85.4 Google
MEGA ResNet-50 77.3 56.5 75.7 85.2 Google

Note: The performance of ResNet-50 backbone are not so stable.

Note: The motion-IoU specific AP evaluation code is a bit different from the original implementation in FGFA. I think the original implementation is really weird so I modify it. So the results may not be directly comparable with the results provided in FGFA and other methods that use MXNet version evaluation code. But we could tell which method is relatively better under the same evaluation protocol.


Please follow for installation instructions.

Data preparation

Please download ILSVRC2015 DET and ILSVRC2015 VID dataset from here. After that, we recommend to symlink the path to the datasets to datasets/. And the path structure should be as follows:


Note: We have already provided a list of all images we use to train and test our model as txt files under directory datasets/ILSVRC2015/ImageSets. You do not need to change them.


Note: Cache files will be created at the first time you run this project, this may take some time! Don't worry!

Note: Currently, one GPU could only hold 1 image. Do not put 2 or more images on 1 GPU!

Note We provide template files named BASE_RCNN_{}gpus.yaml which would automatically change the batch size and other relevant settings. This behavior is similar to detectron2. If you want to train model with different number of gpus, please change it by yourself :) But assure 1 GPU only holds 1 image! That is to say, you should always keep SOLVER.IMS_PER_BATCH and TEST.IMS_PER_BATCH equal to the number of GPUs you use.


The inference command line for testing on the validation dataset:

python -m torch.distributed.launch \
    --nproc_per_node 4 \
    tools/ \
    --config-file configs/MEGA/vid_R_101_C4_MEGA_1x.yaml \
    --motion-specific \
    MODEL.WEIGHT MEGA_R_101.pth 

Please note that:

  1. If your model's name is different, please replace MEGA_R_101.pth with your own.
  2. If you want to evaluate a different model, please change --config-file to its config file and MODEL.WEIGHT to its weights file.
  3. If you do not want to evaluate motion-IoU specific AP, simply deleting --motion-specific.
  4. Testing is time-consuming, so be patient!
  5. As testing on above 170000+ frames is toooo time-consuming, so we enable directly testing on generated bounding boxes, which is automatically saved in a file named predictions.pth on your training directory. That means you do not need to run the evaluation from the very start every time. You could access this by running:
    python tools/ \
        --config-file configs/MEGA/vid_R_101_C4_MEGA_1x.yaml \
        --prediction [YOUR predictions.pth generated by MEGA]


The following command line will train MEGA_R_101_FPN_1x on 4 GPUs with Synchronous Stochastic Gradient Descent (SGD):

python -m torch.distributed.launch \
    --nproc_per_node=4 \
    tools/ \
    --master_port=$((RANDOM + 10000)) \
    --config-file configs/MEGA/vid_R_101_C4_MEGA_1x.yaml \
    --motion-specific \
    OUTPUT_DIR training_dir/MEGA_R_101_1x

Please note that:

  1. The models will be saved into OUTPUT_DIR.
  2. If you want to train MEGA and other methods with other backbones, please change --config-file.
  3. For training FGFA and DFF, we need pretrained weight of FlowNet. We provide the converted version here. After downloaded, it should be placed at models/. See config/ and the code for further details.
  4. For training RDN, we adopt the same two-stage training strategy as described in its original paper. The first phase should be run with config file configs/RDN/vid_R_101_C4_RDN_base_1x.yaml. For the second phase, MODEL.WEIGHT should be set to the filename of the final model of the first stage training. Or you could rename the model's filename to RDN_base_R_101.pth and put it under models/ and directly train the second phase with config file configs/RDN/vid_R_101_C4_RDN_1x.yaml.
  5. If you do not want to evaluate motion-IoU specific AP at the end of training, simply deleting --motion-specific.

Demo Usage

Please follow demo/ to see how to visualize your own images or video.


If you want to use these methods on your own dataset or implement your new method. Please follow

Contributing to the project

Any pull requests or issues are welcomed.