Deep neural networks are extremely powerful models with hundreds and thousands of learnable parameters. The current scenario of training a coloring network presents a new set of challenges, some of which are discussed as follows:

• The current network seems to have learned high-level features, such as grass and sports jerseys (to a certain extent), while it found learning color patterns for smaller objects a bit too difficult.
• The training set was limited to a very specific subset of images and hence that is reflected in the test dataset. The model has poor performance on objects that are either not present in the training set or not many samples contain them.
• Even though the training loss seems to have stabilized in under 50 epochs, we see that the model's performance on coloring is quite poor unless trained for a few hundred epochs.
• The model has a high tendency to color most objects gray or sepia. This is observed in models trained for fewer epochs.

Apart from these challenges, the computation and memory requirements are also quite high for such a complex architecture.