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DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation (ICCV 2019)

paper.pdf

Overview

Requirements:

• PyTorch 1.1.0
• matplotlib 3.0.2
• not in requirements.txt:
  • cython
  • torchvision 0.2.2 or 0.3.0 if cuda10.0
  • pycocotools # (2.0-py3.7-linux-x86_64)
  • maskrnn-benchmark
  • pyyaml yacs
  • opencv-python scikit-image
  • easydict prettytable lmdb tabulate

Installation

• Follow INSTALL.md

Data

YouTube-VOS

• Download the YouTube-VOS dataset from their website. Please note that our code is trained and tested only on YouTube-VOS dataset for 2018 version. There is a newer version released 2019 but it is not tested.

• We recommend to symlink the path to the youtube dataset to datasets/ as follows

cd datasets 
ln -s path/to/youtubeVOS youtubeVOS 

• The files structure should look like:

DMM/datasets 
       ├── youtubeVOS
       │       ├── train 
       │       │      ├── JPEGImages
       │       │      │        ├── ... 
       │       │      ├── Annotations 
       │       │      │        ├── ... 
       │       ├── valid 
       │       │      ├── JPEGImages
       │       │      │        ├── ... 
       │       │      ├── Annotations 
       │       │      │        ├── ... 
       │       ├── train_testdev_ot (optional)
       │       │      ├── JPEGImages
       │       │      │        ├── ... 
       │       │      ├── Annotations 
       │       │      │        ├── ... 

• the train_testdev_ot data can be downloaded from link

Prepare proposals

Option1: Download the extracted file

for evaluation

• To eval DMMnet on youtubeVOS with the fine-tuned proposal net, use the propsoals generated by our fine-tuned Mask R-CNN model:
  • proposals-train-val

  mkdir -p experiments/proposals/ 
  cd experiments/proposals/  
  wget https://www.cs.toronto.edu/~xiaohui/dmm/proposals/proposals_ytb_train.tar.gz
  tar xzf proposals_ytb_train.tar.gz 
  

for training

• To train the DMMnet on youtubeVOS train-train split, need to prepare 1. proposals for both train-train and train-val split extracted by coco pretrained X101 Mask R-CNN model

• proposals can be downloaded:

  • proposals_coco81 (train-train, train-val and testdev-online-training)

   mkdir -p experiments/proposals/  
   cd experiments/proposals/ 
   wget http://www.cs.toronto.edu/~xiaohui/dmm/proposals/feature_coco81.tar.gz
   tar xzf feature_coco81.tar.gz 
  

• preprocess the proposals for training DMM:

python src/tools/reduce_pth_size_by_videos.py  experiments/proposals/coco81/inference/youtubevos_train3k_meta/predictions.pth  train 50
python src/tools/reduce_pth_size_by_videos.py  experiments/proposals/coco81/inference/youtubevos_val200_meta/predictions.pth  trainval 50
python src/tools/reduce_pth_size_by_videos.py  experiments/proposals/coco81/inference/youtubevos_testdev_online_meta/predictions.pth  train_testdev_ot 90 

• The files structure should look like:

DMM/experiments
       ├── propnet
       │     ├── join_ytb_bin
       │     │       ├── model_0172500.pth 
       │     ├── online_ytb
       │     │       ├── model_0225000.pth 
       ├── dmmnet 
       │     ├── ytb_255_50_matchloss_epo13
       │     │       ├── epo13_iter01640
       │     ├── ytb_255_50
       │     │       ├── epo08_iter01640
       │     ├── online_ytb
       │     │       ├── epo101 
       ├── proposals 
       │     ├── coco81 
       │     │     ├── inference
       │     │     │       ├── youtubevos_train3k_meta (optional)
       │     │     │       ├── youtubevos_val200_meta
       │     │     │       ├── youtubevos_testdev_online_meta (optional)
       │     ├── ytb_train 
       │     │     ├── inference
       │     │     │       ├── youtubevos_val200_meta
       │     ├── ytb_ot 
       │     │     ├── inference
       │     │     │       ├── youtubevos_testdev_meta

Option2: extract the proposals

• The model trained on youtubeVOS dataset can be found in MODEL_ZOO.md
• The scripts used to extract proposals from the trained model can be found in scripts/extract/

Training

• Train DMMnet on youtubeVOS:

sh scripts/train/train_101.sh 
# or scripts/train/train_50.sh # for resnet 50 mode  

Online training

Train DMMnet on the first frame of validation set,

• first download the preprocessed data used for online training from here, extract the data and put/link the extracted folder as /PATH/TO/datasets/youtubeVOS/train_testdev_ot

• prepare proposal, check the Section: Prepare proposals - for training

• get the DMMnet trained on train-train set for 1 epoch from here and put it under experiments/dmmnet/

• start online training

sh scripts/train/train_online.sh # it takes ~0.17h for one epoch

Evaluation

• Evaluate DMMnet on trainval split:
  • will need the trained model and the extracted train-val proposal:

  cd ./experiments/dmmnet/
  wget http://www.cs.toronto.edu/~xiaohui/dmm/models/dmmnet_ytb_255_50_matchloss_epo13.tar.gz 
  tar xzf dmmnet_ytb_255_50_matchloss_epo13.tar.gz 
  wget http://www.cs.toronto.edu/~xiaohui/dmm/models/dmmnet_ytb_255_50.tar.gz 
  tar xzf dmmnet_ytb_255_50.tar.gz
  cd ../../ 
  
  cd ./experiments/proposals/ 
  wget http://www.cs.toronto.edu/~xiaohui/dmm/proposals/proposals_ytb_train.tar.gz
  tar xzf proposals_ytb_train.tar.gz 
  cd ../../
  

  • run sh scripts/eval/eval_r50.sh
  • compute the J and F score by sh scripts/metric/full_eval.sh /PATH/TO/OUTPUT/merged/ expected results:

• Evaluate online-trained DMMnet:
  • Download proposals extracted by online-trained proposal net:

  cd ./experiments/proposals/
  wget http://www.cs.toronto.edu/~xiaohui/dmm/proposals/proposals_ytb_ot.tar.gz 
  tar xzf proposals_ytb_ot.tar.gz 
  cd ../../
  

  • Download model:

  cd experiments/dmmnet/
  wget http://www.cs.toronto.edu/~xiaohui/dmm/models/dmmnet_online_ytb.tar.gz 
  tar xzf dmmnet_online_ytb.tar.gz 
  cd ../../
  

  • run scripts/eval/eval_testdev.sh
  • prepare the submission data with scripts/submit.sh and submit to the server, expected resules: G mean = 0.579

part of the code is from https://github.com/imatge-upc/rvos