You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I have not trained myself with custom datasets, but I have tested my trained model on random images taken from the internet. However, if you want to train on custom data, these are the things to keep in mind:
The first step would be to make your dataset compatible with the COCO format. A tutorial on how you can make a customized COCO format dataset can be found here.
Then, you have to define a seen-unseen split for the classes of that dataset. Suppose you have that list of S seen and U unseen classes. Define them in splits.py. Make the appropriate changes while loading the data and semantics for these classes.
This file encloses the class for a custom dataset. Make the necessary changes in this. It contains the config parameters. While running any script, you must use this config file from now on.
Train a CNN backbone of your choice (like Resnet-101) on Imagenet classes after removing any class from Imagenet that exists in U. This is strictly as per zero-shot criteria, but if you wish, you can omit this step and directly use the one we provide.
That's it! Now you can follow the steps 1-6 given in the README.
What steps should I take if I want to train with custom data? Thanks in advance.
The text was updated successfully, but these errors were encountered: