We hope you have been following the development around the challenge.
Today, we are finally releasing a baseline, which was promised in the last townhall.
Did you miss the townhall?
You can check about it here: 📹 Town Hall Recording & Resources from top participants
About the Baseline
The baseline contains fast heuristic implementations of some simple ideas.
- Purchase images with more labels - For multilabel datasets, often having images with more than one label gives a boost for deep learning models.
- Purchase uncertain images - Purchase images which have the most uncertainty in their predictions. While many methods exists to measure uncertainty, a simple output probability based heuristic method is used here.
- Purchase images to balance labels - Well balanced datasets can improve model performance in deep learning. We set a uniform target distribution and try to purchase labels to get closer to that distribution. The provided code can try to purchase labels to any target distribution.
Learn more about the implementation here.
Jump to the codebase directly here.
Looking for more resources to explore? Check out this thread.
Ok, talk is easy, how does it perform?
Well, as it stands, the baseline is currently on 5th position on the whole leaderboard.
Do you have additional questions?
Feel free to drop them in this thread and we can reply them asap.
What are you waiting for? Let’s get started with the submissions!
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