Training & Finetuning on Custom Data

Hi,

Apologies if this has been asked before.

I have a custom data with ~700 tagged images, total number of classes are 15.

I have trained a model using the following combination (this gives the best results so far);

python train_dist.py --dataset ADE20K --model EncNet --aux --se-loss --backbone resnest101 --epochs XXX

It is giving okay-ish results. I understand the data is too less to expect pretty good results but the classes are rather simple and I cannot afford more data. I want to fine-tune (or maybe overfit) to my custom data for demonstration purpose. But, whatever I try so far, it just doesn't get any better.

Can you suggest any best practices, suggestions on how can I fine-tune to my particularly small data-set?

Thank you!

The issue names no files, tests, or implementation entry points. Start by reviewing the train_dist.py command and the custom dataset setup described in the report; a useful outcome would need to define and document reliable fine-tuning practices for the small 15-class dataset, but the requested behavior is not specified.