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This is a GRU version of PredNet based on quadjr/PredNet.

ConvLSTM is replaced by ConvGRU, the others are the same as the original version.


Comparing output-images between this version and original one about images in image_02 in of KITTI showed some differences, the effectiveness is under investigation.

Testd on

  • Ubuntu 14.04
  • Python 2.7.6
  • chainer 1.9.1
  • CUDA Toolkit 7.5
  • cuDNN v5


  1. Train
    On a CPU
    $ python -i dataset/train_list.txt
    On a GPU 0 $ python -i dataset/train_list.txt -g 0

Model files and some images will be generated in models/ and images/ directory.
Image suffix means x (input), y (predicted), z (correct).

  1. Test
    $ python -i dataset/test_list.txt --test --initmodel models/???.model
    Plase specify the model file with option --initialmodel.
    Test result will be generated in images/ directory.
    Image suffix means x (input), y (predicted).