Table 2.
Strategic application of sparsity techniques across YOLOv8n architecture.
| Layer | Module | Function | Sparsity strategy |
|---|---|---|---|
| 0-9 | Backbone | Feature extraction | No modification |
| 10-11 | Neck (Upsample + Concat) | Multi-scale fusion | – |
| 12 | Neck (C2f) | Feature refinement | GL applied |
| 13-14 | Neck (Upsample + Concat) | Multi-scale fusion | – |
| 15 | Neck (C2f) | Feature refinement | GL applied |
| 16 | Neck (Conv) | Downsampling | GL applied |
| 17 | Neck (Concat) | Feature fusion | – |
| 18 | Neck (C2f) | Feature refinement | GL applied |
| 19 | Neck (Conv) | Downsampling | GL applied |
| 20 | Neck (Concat) | Feature fusion | – |
| 21 | Neck (C2f) | Feature refinement | GL applied |
| 22 | Head (Detect) | Multi-scale detection | SSS applied |