Transfer learning using the available weights

[Ahsanr312]

Suggest an idea for this project
Hi! I have been following the repository and used the available weights for prediction on my own dataset which resulted in poor segmentation. While thinking about how to achieve better results on my own dataset, I came up with a few ideas as follows:

1. Train the architecture only on my custom dataset
2. Train the architecture on MS COCO + custom dataset
3. Train the architecture using transfer learning on custom dataset

I believe, I won't get good results by choosing first approach whereas the second approach can get me some good results however I am looking for any method for the third approach mentioned above.

Looking forward for hearing from you all.

[jakubczakon]

Hi @Ahsanr312 thank you for the suggestion.

Actually, you can do any of the options right now.
This repository is more about how to train it (how we trained it) than "generalizable" results.
We wanted to share our work on this competition with the world.

One thing to keep in mind is that this was built for buildings so perhaps you should use a different dataset than MS COCO (cityscape is a good one I believe).

Good luck with fine-tuning!

[Ahsanr312]

I have looked for steps to fine-tune the model but couldn't fine anything.
Can you help me with it? @jakubczakon

[data-overload]

@Ahsanr312 Were you able to figure out how to tune the model?
I prepared the training directory as per the Readme with my own images+annotations but get a DecodeError running the train command.
Edit: that error is fixed by upgrading neptune client, however I still struggle to produce new tuned weights (no errors but I don't see the output from training).