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CapsuleVOS

This is the code for the ICCV 2019 paper CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing.

Arxiv Link: https://arxiv.org/abs/1910.00132

The network is implemented using TensorFlow 1.4.1.

Python packages used: numpy, scipy, scikit-video

Files and their use

  1. caps_layers_cod.py: Contains the functions required to construct capsule layers - (primary, convolutional, and fully-connected, and conditional capsule routing).
  2. caps_network_train.py: Contains the CapsuleVOS model for training.
  3. caps_network_test.py: Contains the CapsuleVOS model for testing.
  4. caps_main.py: Contains the main function, which is called to train the network.
  5. config.py: Contains several different hyperparameters used for the network, training, or inference.
  6. inference.py: Contains the inference code.
  7. load_youtube_data_multi.py: Contains the training data-generator for YoutubeVOS 2018 dataset.
  8. load_youtubevalid_data.py: Contains the validation data-generator for YoutubeVOS 2018 dataset.

Data Used

We have supplied the code for training and inference of the model on the YoutubeVOS-2018 dataset. The file <code>load_youtube_data_multi.py</code> and <code>load_youtubevalid_data.py</code> creates two DataLoaders - one for training and one for validation. The <code>data_loc</code> variable at the top of each file should be set to the base directory which contains the frames and annotations.

To run this code, you need to do the following:

  1. Download the YoutubeVOS dataset
  2. Perform interpolation for the training frames following the papers' instructions

Training the Model

Once the data is set up you can train (and test) the network by calling <code>python3 caps_main.py</code>.

The <code>config.py</code> file contains several hyper-parameters which are useful for training the network.

Output File

During training and testing, metrics are printed to stdout as well as an output*.txt file. During training/validation, the losses and accuracies are printed out to the terminal and to an output file.

Saved Weights

Pretrained weights for the network are available here. To use them for inference, place them in the <code>network_saves_best</code> folder.

Inference

If you just want to test the trained model with the weights above, run the inference code by calling <code>python3 inference.py</code>. This code will read in an .mp4 file and a reference segmentation mask, and output the segmented frames of the video to the Output folder.

An example video is available in the Example folder.