Checklist IOAI 2025 At-Home Round · Task 1
Radar
Build a model that marks, for every cell of a radar range-azimuth map, whether a human is present, using six radar heatmaps.
The task
Radar locates objects from reflected signals, but noise and clutter such as trees make detection unreliable. This practice task asks for an AI model that finds where humans are in a radar's field of view.
Each sample is a 7 × 50 × 181 tensor: six normalised heatmaps (static and dynamic range-azimuth, range-elevation and range-velocity maps) and a range-azimuth label map in which 1 means a human is present and 0 means absent.
Given the six heatmaps, the model must output the 50 × 181 label map. Correct human cells count far more than correct background cells. The on-site Individual Contest Radar task later extended this problem to five classes.
Abridged by SOTA from the official materials. The official statement has the exact rules, and it wins wherever this summary differs.
At a glance
- You get
- Samples stored as
.mat.pttensors of shape 7×50×181 (six heatmaps plus a binary label map); training and validation sets downloadable from Google Drive asMillimeter-wave_dataset.zip. - You submit
- A 50×181 binary map per sample. The baseline notebook submits
submission.zipcontaining the model definition (submission_model.py) and its weights (submission_dic.pth). - Scoring
- Weighted accuracy: each correctly identified background point earns 1 point and each correctly identified target point earns 1,500 points; the total is normalised to 0-1 by the maximum possible score. (The repo's scoring script gives 5,000 points per target point instead.)
- Format
- At-home round (non-graded practice, 1-30 July 2025)