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July 2020

tl;dr: Class rebalance of minority helps in object detection for nuscenes dataset.

Overall impression

The class balanced sampling and class-grouped heads are useful to handle imbalanced object detection.

Key ideas

  • DS sampling:
    • increases sample density of rare classes to avoid gradient vanishing
    • count instances and samples (frames). Resample so that samples for each class is on the same order of magnitude.
  • Class balanced grouping: each group has a separate head.
    • Classes of similar shapes or sizes should be grouped.
    • Instance numbers of diff groups should be balanced properly.
    • Supergroups:
      • cars (majority classes)
      • truck, construction vehicle
      • bus, trailer
      • barrier
      • motorcycle, bicycle
      • pedestrian, traffic cone
  • Fit ground plane and plant GT back in.
  • Bag of tricks
    • Accumulate 10 frames (0.5 seconds) to form a dense lidar BEV
    • AdamW + One cycle policy

Technical details

  • Regress vx and vy. If bicycle speed is above a certain thresh, then it is with rider.

Notes

  • Questions and notes on how to improve/revise the current work