Benchmarking ResNet Backbones in RT-DETR: Impact of Depth and Regularization under Environmental Conditions
A new research paper published on arXiv presents a comparative evaluation of the RT-DETR object detection model, specifically focusing on its performance in competitive robotics environments. The study addresses a gap in existing literature regarding how backbone scale and environmental settings affect transformer-based detectors. Researchers tested four ResNet backbones (ResNet18, ResNet34, ResNet50, and ResNet101) under varying lighting and background contrast conditions while analyzing the impact of dropout rates on confidence and accuracy. The findings reveal that environmental changes primarily influence prediction confidence rather than inference latency or classification accuracy, which remained consistently high. The study identifies distinct optimal architectures for different conditions: ResNet50 offers the best trade-off under illumination variations, achieving near-perfect accuracy with low latency, while ResNet34 provides the most balanced performance under background variations. These results suggest that intermediate-depth models are most effective for balancing performance and efficiency in real-time robotic applications, highlighting the need for architecture selection based on specific environmental challenges.
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Benchmarking ResNet Backbones in RT-DETR: Impact of Depth and Regularization under Environmental Conditions
A new research paper published on arXiv presents a comparative evaluation of the RT-DETR object detection model, specifically focusing on its performance in competitive robotics environments. The study addresses a gap in existing literature regarding how backbone scale and environmental settings affect transformer-based detectors. Researchers tested four ResNet backbones (ResNet18, ResNet34, ResNet50, and ResNet101) under varying lighting and background contrast conditions while analyzing the impact of dropout rates on confidence and accuracy. The findings reveal that environmental changes primarily influence prediction confidence rather than inference latency or classification accuracy, which remained consistently high. The study identifies distinct optimal architectures for different conditions: ResNet50 offers the best trade-off under illumination variations, achieving near-perfect accuracy with low latency, while ResNet34 provides the most balanced performance under background variations. These results suggest that intermediate-depth models are most effective for balancing performance and efficiency in real-time robotic applications, highlighting the need for architecture selection based on specific environmental challenges.
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