YOLOv9, the latest version in the YOLO series authored by Chien-Yao Wang and team, was launched on February 21, 2024. It represents a significant advancement from YOLOv7, also developed by Chien-Yao Wang and colleagues. While YOLOv7 improved training efficiency through a trainable bag-of-freebies, it did not directly address the challenge of information loss during the feedforward process, known as the information bottleneck. This limitation stems from down-sampling operations in the network, which can lead to the loss of crucial input data.
Existing solutions such as reversible architectures, masked modeling, and deep supervision help alleviate the information bottleneck issue, but they come with various drawbacks in both training and inference phases. Additionally, they are less effective for smaller model architectures, which are essential for real-time object detection systems like those in the YOLO series.
To overcome these challenges, YOLOv9 introduces two innovative techniques: Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). These methods aim to directly address the information bottleneck problem, thereby enhancing the accuracy and efficiency of object detection.
Comparison of YOLOv9 with SOTA Models
The COCO dataset benchmarks reveal that YOLOv9 achieves better object detection performance, striking a favorable balance between efficiency and accuracy across its different versions. Yolov9 model architecture surpasses popular existing YOLO models like YOLOv8, YOLOv7, and YOLOv5 in terms of achieving a higher mean Average Precision (mAP) when evaluated against the MS COCO dataset.
YOLOv9 achieves exceptional accuracy with lower resources
• The GELAN(Generalized Efficient Layer Aggregation Network) architecture in the model enhances accuracy while reducing the number of parameters and computational needs.
• The PGI(Programmable Gradient Information) training method improves learning gradients' reliability, particularly benefiting smaller models.
GELAN(Generalized Efficient Layer Aggregation Network)
YOLOv9 keeps the YOLO family's reputation for fast processing with a new setup called GELAN, which mixes the best parts of CSPNet and ELAN. CSPNet is great at managing data flow to pull out important features effectively, while ELAN focuses on quick processing using layers stacked on top of each other. GELAN combines these features, offering a design that's not only lightweight and speedy but also accurate. It improves upon ELAN by letting it stack various types of processing blocks, not just layers, enhancing the speed and efficiency of the model across all its parts.