With the introduction of the new YOLOv9 model, the question arises: Is it superior to YOLOv8? It's crucial to recognize that the release of a new model doesn't guarantee its superiority across all scenarios or projects. Extensive testing is essential, encompassing custom datasets, pretrained models, varied image sizes, and diverse use-cases. It's important to understand that one model isn't inherently superior to the other; effectiveness depends on factors such as the dataset, use-case, and specific situation. While benchmarks may indicate performance differences, it's imperative not to rely solely on them. Thorough testing is key to determining the most suitable model for a given application.
What is YOLO?
Before we dive into the comparison, let’s briefly recap what YOLO is all about. You Only Look Once (YOLO) is a single-shot object detection architecture that predicts bounding boxes and class probabilities directly from an input image. It differs from the approach taken by previous object detection algorithms, which repurposed classifiers to perform detection. It processes the entire image in one forward pass, making it efficient for real-time applications.
Fig 1. YOLO Pipeline
Several new versions of the same model have been proposed since the initial release of YOLO in 2015.
Fig 2. YOLO Timeline
YOLOv8
YOLOv8 was introduced in 2022 by Ultralytics. YOLOv8 is built on the YOLOv5 framework and includes several architectural and developer experience improvement. YOLOv8 gained popularity for its balance between speed and accuracy. It had faster inference and it maintains real-time performance, making it suitable for applications requiring low latency. YOLOv8 captures a higher proportion of true positives while minimizing false positives effectively. Its precision-recall curve demonstrates its superiority in terms of both precision and recall. YOLOv8 allows fine-tuning on custom datasets. Users can train it on specific object classes relevant to their application.
Segmentation
YOLOv8 surprises us with its instance segmentation capabilities. While YOLOv9 focuses primarily on object detection, YOLOv8 can also segment objects at the pixel level. This feature is invaluable for tasks like semantic segmentation and medical imaging.
Pose Estimation
Pose estimation, often overlooked, is a critical aspect of computer vision. YOLOv8 can estimate the orientation or pose of detected objects. Imagine using it to track yoga poses, analyze sports movements, or enhance augmented reality applications.
YOLO-World
The YOLO-World Model presents a cutting-edge, real-time methodology for Open-Vocabulary Detection tasks. This advancement allows the identification of objects in images using descriptive texts. YOLO-World stands out as a versatile tool for various vision-based applications by substantially reducing computational requirements while maintaining competitive performance.
YOLOv9
YOLOv9 builds upon the legacy of previous versions, introducing architectural enhancements. Here’s what sets it apart: YOLOv9 incorporates advancements like Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN).PGI prevents data loss during gradient updates, while GELAN optimizes lightweight models through gradient path planning. Through the incorporation of PGI and the adaptable GELAN architecture, YOLOv9 not only boosts the model's learning capabilities but also guarantees the preservation of vital information throughout the detection process. The progress of YOLOv9 is fundamentally centered around tackling the issues arising from information loss in deep neural networks. Its design incorporates the Information Bottleneck Principle and employs Reversible Functions innovatively, ensuring that YOLOv9 sustains both high efficiency and accuracy.
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.