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YOLOv6

One of the version of YOLO object detection algorithm is YOLOv6 that was introduced in 2021 and also improved and built on top of YOLOv5 by introducing new features including, anchor-free object detection[3] that helps us avoiding need for anchor boxes as well as regression[4], new classification[5] losses for preventing overfitting problems while training the model.YOLOv6 uses a new backbone called EfficientRep, which is based on RepVGG. EfficientRep is more parallel than previous YOLO backbones, which makes it more efficient to train and deploy. The neck of YOLOv6 is also enhanced with RepBlocks or CSPStackRep to further improve efficiency.
YOLOv6 uses a new label assignment method called Task alignment learning which is introduced in [6]. This method assigns labels to objects in a way that is more consistent with the tasks of object detection and classification. Finally, YOLOv6 uses self-distillation to improve accuracy. Self-distillation is a technique where the model is trained on its own predictions. This helps the model to learn from its own mistakes and improve its performance. In short, YOLOv6 is a new object detection model that offers significant improvements in terms of efficiency and accuracy. It uses a new backbone, neck, and label assignment method, as well as self-distillation, to achieve these improvements.
YOLOv6 Architectural Improvements. The YOLO (You Only Look Once) object detection model has been iteratively improved to predict bounding boxes around selected objects accurately and in real time. The better the model, the less hardware is required to train and deploy it. The YOLO model takes an input image and passes it through a series of convolutional layers in the backbone. The backbone extracts features from the image. The features are then passed through the neck, which is a set of layers that further refine the features “Fig.3”.
The neck features are then passed through three heads:
• The object head predicts the probability that each bounding box contains an object.
• The class head predicts the class of each object.
• The box regression head predicts the coordinates of each bounding box.
The YOLO model is a powerful object detection model that has been used in a variety of applications, such as self-driving cars, video surveillance, and augmented reality.

Fig. 3. YOLO architecture as depicted in PP-YOLO

YOLOv6 is an improved version of YOLO that iterates on the backbone and neck by redesigning them with the hardware in mind. The model introduces two new components: the EfficientRep Backbone and the Rep-PAN Neck “Fig.4” [11].

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