Thread by @ai_fast_track

4 types of imbalance issues in object detection that you should know:

Here is a brief description of each one of them and some potential solutions.

• Scale imbalance
• Objective imbalance
• Class imbalance
• Spatial imbalance

🔸 Scale imbalance
It happens when the objects have different sizes with different numbers of objects: e.g. small objects vs. big objects.

✅ Potential Solution
• Oversample small objects using the Copy&Paste data augmentation
• Use higher resolution images

🔸 Objective imbalance
It happens when calculating a total loss (classification and regression losses). One loss might dominate another.

✅ Potential Solution
• Use the weighted loss

🔸 Class imbalance:
It might be related to either:
1- Foreground-Background imbalance classes, or
2- Foreground-Foreground (positive) classes: e.g. Person class vs Parking Meter class in the COCO dataset

✅ Potential Solution:
• For Case 1: Use the Focal Loss
• For Case 2: Oversample under-represented classes

🔸 Spatial imbalance:
It refers to a set of factors related to the spatial properties of the bounding boxes such as regression penalty, location, and IoU.

For example, the L2 loss penalizes more severely shifted predicted boxes than the L1 or Smooth L1 losses.

✅ Potential Solution:
• Use L1 or Smooth L1 Loss
• Use anchor-free models

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