The paper "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information" addresses the issue of information loss during deep network processing, which can lead to biased gradient flows and incorrect predictions. The authors propose Programmable Gradient Information (PGI) to address this problem by generating reliable gradients through an auxiliary reversible branch, ensuring that deep features retain key characteristics for the target task. They also introduce the Generalized Efficient Layer Aggregation Network (GELAN), a lightweight network architecture based on gradient path planning. GELAN combines conventional convolution operators to achieve better parameter utilization compared to depth-wise convolution-based designs. The proposed YOLOv9, which integrates PGI and GELAN, outperforms existing real-time object detectors on the MS COCO dataset, demonstrating superior performance in terms of accuracy, parameter usage, and computational efficiency. The paper provides detailed experimental results and ablation studies to validate the effectiveness of the proposed methods.The paper "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information" addresses the issue of information loss during deep network processing, which can lead to biased gradient flows and incorrect predictions. The authors propose Programmable Gradient Information (PGI) to address this problem by generating reliable gradients through an auxiliary reversible branch, ensuring that deep features retain key characteristics for the target task. They also introduce the Generalized Efficient Layer Aggregation Network (GELAN), a lightweight network architecture based on gradient path planning. GELAN combines conventional convolution operators to achieve better parameter utilization compared to depth-wise convolution-based designs. The proposed YOLOv9, which integrates PGI and GELAN, outperforms existing real-time object detectors on the MS COCO dataset, demonstrating superior performance in terms of accuracy, parameter usage, and computational efficiency. The paper provides detailed experimental results and ablation studies to validate the effectiveness of the proposed methods.