Tracking via Colorization

To understand the concepts read this blog.

Introduction

This is a pytorch implementation of Tracking Emerges by Colorizing Videos, a self supervised tracking model.

How to use

Training

Use the config file to setup the hyperparameters for the training

• Clustering

  # modify these config for image quantisations keep KMEANS_FILE=None for 1st run,
  # for next run you can reuse the created file
    KMEANS_FILE = None
    CLUSTERS = 16
    CHANNELS = 'lab'
    QUANTIZE_CHANNELS = (1,2)
    KMEANS_SAMPLES = 100000
    KMEANS_REFIT = False

• train

python train.py

Model logging

In this experiment we have used wandb to plot model metrics and intermediate outputs, you can have a quick start here. Create an account and start using this by setting your api key.

export WANDB_KEY= <your api key>

and then change WANDB=True in config

 WANDB = False
 WANDB_LOG_ROOT = "./logs/model"
 __WANDB_KEY = os.getenv("WANDB_KEY")

TODO

You can start training your model with this code but it is still in WIP phase

• Add requirements.txt
• Add testing set loss during training
• Add more model metrics
• Create tracking inference code
• Create image up-sampling logic for precise mask prediction