First Prize - International Hackathon by Perforated AI

Object detection is fundamental to numerous real-world applications including autonomous vehicles, surveillance systems, robotics, and medical imaging. YOLOv11n represents the latest evolution in the YOLO family, designed for real-time detection with high accuracy.

Improving the accuracy of an object detection system matters because:

• Safety-Critical Applications: In autonomous driving and medical imaging, even small improvements in detection accuracy can prevent accidents or catch diseases earlier, potentially saving lives.
• Data Efficiency: Many real-world scenarios have limited labeled data. If dendrites can achieve higher accuracy with the same data (or match baseline accuracy with less data), this reduces annotation costs and enables deployment in data-scarce domains.
• Resource Optimization: Better accuracy with the same model architecture means organizations don't need to scale up to larger, more expensive models to achieve their performance targets.
• Scalability: Improvements on benchmark datasets like VOC2007 typically transfer to custom datasets, making this research applicable across industries from retail to agriculture to manufacturing.

We demonstrated the application of PerforatedAI's dendritic neural network optimization to YOLOv11n object detection on the Pascal VOC2007 dataset. The goal was to prove that dendrite-enhanced networks can achieve higher accuracy on the same data compared to traditional neural networks.

We implemented a custom training loop that integrates PAI's dendritic optimization with Ultralytics' YOLOv11n, enabling:

• Adaptive plateau detection (DOING_HISTORY mode) for optimal dendrite addition using Open Source PAI Dendrites
• Data efficiency experiments at multiple training set sizes (100%, 50%)
• Reproducible results with seed=42 and deterministic settings

Key Implementation Details

• Custom Training Loop: We implemented a custom training loop because Ultralytics' default YOLO.train() cannot be used with PAI - add_validation_score() must be called every epoch.
• Module Configuration: We configured PAI to add dendrites to YOLO's feature extraction blocks (C3k2, C3k, C2PSA, Bottleneck, PSABlock) while tracking but not modifying normalization layers.
• Reproducibility: All experiments use seed=42 with deterministic settings for reproducibility.

Dataset: Pascal VOC2007

• 20 object classes
• ~5,000 training images
• ~5,000 test images
• Standard object detection benchmark

• Secured 1st place globally among 693 participants
• Our submission was officially adopted as a base example in PerforatedAI's official GitHub repository
• Demonstrated practical application of dendritic neural networks in production-ready object detection