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YOLO-KAN: What the Ablation Experiments Taught Me

A concise research note on introducing Kolmogorov-Arnold Network modules into YOLO11n, testing flatten strategies, and reading the resulting trade-offs.

Computer VisionKANYOLOResearch
YOLO-KAN: What the Ablation Experiments Taught Me cover

The goal of this research was deliberately narrow: improve YOLO11n accuracy while reducing network depth. Kolmogorov-Arnold Networks (KANs) were a promising candidate because they replace fixed node activations with learnable functions on edges, potentially extracting richer features without simply stacking more layers.

From an MLP block to a KAN block

I introduced KAN modules into the YOLO backbone, then compared several ways of serializing feature maps before they entered the KAN layer:

  • depthwise flattening;
  • max-pool flattening; and
  • convolutional flattening with different kernel sizes.

This detail mattered more than expected. The flatten structure materially changed both the accuracy curve and the spatial features emphasized by the detector.

What the experiments showed

The baseline YOLO11n reached 63.99% precision. The strongest configuration, KAN-2-5, reached 65.83%, while KAN-2-7 produced the highest mAP@50 at 54.48%. Across the experiments, the largest precision gain was 1.84 percentage points.

The simplified architecture also reduced the layer count from 319 to 299. That result supported the original hypothesis: the KAN structure could improve feature extraction without depending on a deeper network.

Heatmaps revealed the trade-off

The heatmaps made the model behavior easier to interpret. YOLO11n focused mainly on the train's most obvious features. KAN-2-5 attended to both the train and useful background context. With the larger KAN-2-7 receptive field, attention spread further into non-primary regions.

That made KAN-2-5 the best overall balance in this study—not because it won every metric, but because it improved accuracy without losing focus.

The practical lesson

Adding a new module is only the beginning of an architecture experiment. How data is reshaped before the module can determine whether the module works at all. In this project, flatten-layer design was as important as the KAN block itself.

Explore the project page and complete research poster, or open the original PDF.