In one of our previous posts, we explored how Aiminify can be used to compress YOLO models efficiently. In this experiment, we applied the same approach to the new YOLO26 model and evaluated how far we could push compression using pure pruning.
The results show that even with aggressive compression, it’s possible to significantly reduce model size and compute while maintaining strong performance.
Setup
For this experiment we prune YOLO26s, the small version of the latest YOLO models by Ultralytics, with our level 5 compression. This is the most aggressive pruning setting currently available in Aiminify.
We kept the setup intentionally simple:
• Dataset: COCO
• Training: 100 epochs
• Techniques: pruning + standard fine-tuning
• No data augmentation or additional optimization tricks
This allows us to isolate the effect of compression itself.
Results
Below is a comparison between the original YOLO26s model and the compressed version:
| Metric | YOLO26s | Compressed | Change |
|---|---|---|---|
| GFLOPS | 22.8 | 17.7 | -22% |
| Parameters | 10,000,000 | 7,500,000 | -25% |
| mAP50 | 0.641 | 0.590 | -8% |
| mAP50-95 | 0.476 | 0.428 | -10% |
| Precision | 0.693 | 0.680 | -2% |
| Recall | 0.583 | 0.538 | -8% |
Discussion
The results show a clear trade-off between efficiency and accuracy. With relatively aggressive pruning, we are able to significantly reduce model size and compute while keeping performance within a practical range.
The compression reduces:
• 25% of parameters
• 22% of compute (GFLOPs)
At the same time, the accuracy impact remains relatively limited:
• Precision is almost unchanged (-2%)
• mAP drops by 8–10%
• Recall decreases slightly (-8%)
This trade-off is often acceptable in real-world deployments, especially where latency, memory, or cost constraints are critical. The model is also still trained on all the different classes in the COCO data set, while in practice way fewer classes are used. When you fine tune a compressed model on less classes, the performance will be even better.
Conclusion
With only pruning and basic fine-tuning, we were able to:
• Decrease model size by 25%
• Reduce compute by 22%
• Maintain competitive detection performance
Showing that model compression can solve latency or memory issues while keeping good performance.
If you’re interested in testing the compressed or fine-tuned YOLO26s model yourself, feel free to reach out.