YOLO models have become ubiquitous in the world of deep
learning, computer vision, and object detection. If you are working on object
detection, then there is a high chance that you have used one of the many YOLO
models at some point. In this blog post, we will explore the latest and perhaps
the best YOLO model to date, that is, YOLOv6.
YOLOV6 is perhaps the BEST and most improved version of the YOLO models. It has delivered highly impressive results and excelled in terms of detection accuracy and inference speed.
How Does YOLOv6 Work?
YOLOv6 employs plenty of new approaches to achieve state-of-the-art results. These can be summarized into four points:
- Anchor free: Hence provides better generalizability and costs less time in post-processing.
- The model architecture: YOLOv6 comes with a revised reparameterized backbone and neck.
- Loss functions: YOLOv6 used Varifocal loss (VFL) for classification and Distribution Focal loss (DFL) for detection.
- Industry handy improvements: Longer training epochs, quantization, and knowledge distillation are some techniques that make YOLOv6 models best suited for real-time industrial applications.
YOLOv6 Model Architecture
Several modern and state-of-the-art practical techniques have been used to make all the YOLOv6 models as fast and accurate as possible.
As with any other YOLO model, the YOLOV6 too has three components. They are the Backbone, the Neck, and the Head of the network, and all have something new to offer. As mentioned earlier, one of the biggest aspects of YOLOv6 is that it is anchor free and uses a reparameterized backbone!
The following image is a complete display of the YOLOv6 object detection model architecture.
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| YOLOv6 model architecture. Ref: learnopencv |
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