Basically, the goal is to predict the speed of a car from a video.

data/train.mp4 is a video of driving containing 20400 frames. Video is shot at 20 fps. data/train.txt contains the speed of the car at each frame, one speed on each line.

data/test.mp4 is a different driving video containing 10798 frames. Video is shot at 20 fps. The deliverable is test.txt

python main.py data/train.mp4 data/train.txt --mode=train --split=0.3

If you'd like to continue training using the pretrained network then add the --resume flag to that line.
If any modifications are made to the optical flow part of the model then --wipe must be used to reprocess the data

python main.py data/train.mp4 data/train.txt --mode=eval

This will print the mean squared error.

python main.py data/train.mp4 data/train.txt --mode=play

If you want a more graphical display you can use the play mode. This will output the Optical Flow video with prediction overlay.

python main.py data/test.mp4 data/test.txt --mode=test

It will infer the model and save the predicted value to test.txt file.

I divided flow_data in 70-30 ratio in train and validation set.
I got a MSE of around 2.5 on train data and 0.45 on validation data. MSE on entire train data is 0.55.