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ultralytics 8.0.141 create new SettingsManager (#3790)

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Glenn Jocher 2023-07-23 16:03:34 +02:00 • committed by GitHub
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14
docs/yolov5/tutorials/architecture_description.md
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@ -160,9 +160,9 @@ The objectness losses of the three prediction layers (`P3`, `P4`, `P5`) are weig
The YOLOv5 architecture makes some important changes to the box prediction strategy compared to earlier versions of YOLO. In YOLOv2 and YOLOv3, the box coordinates were directly predicted using the activation of the last layer.
![b_x](https://latex.codecogs.com/svg.image?b_x=\sigma(t_x)+c_x)
![b_y](https://latex.codecogs.com/svg.image?b_y=\sigma(t_y)+c_y)
![b_w](https://latex.codecogs.com/svg.image?b_w=p_w\cdot&space;e^{t_w})
![b_x](https://latex.codecogs.com/svg.image?b_x=\sigma(t_x)+c_x)
![b_y](https://latex.codecogs.com/svg.image?b_y=\sigma(t_y)+c_y)
![b_w](https://latex.codecogs.com/svg.image?b_w=p_w\cdot&space;e^{t_w})
![b_h](https://latex.codecogs.com/svg.image?b_h=p_h\cdot&space;e^{t_h})
<img src="https://user-images.githubusercontent.com/31005897/158508027-8bf63c28-8290-467b-8a3e-4ad09235001a.png#pic_center" width=40%>
@ -171,9 +171,9 @@ However, in YOLOv5, the formula for predicting the box coordinates has been upda
The revised formulas for calculating the predicted bounding box are as follows:
![bx](https://latex.codecogs.com/svg.image?b_x=(2\cdot\sigma(t_x)-0.5)+c_x)
![by](https://latex.codecogs.com/svg.image?b_y=(2\cdot\sigma(t_y)-0.5)+c_y)
![bw](https://latex.codecogs.com/svg.image?b_w=p_w\cdot(2\cdot\sigma(t_w))^2)
![bx](https://latex.codecogs.com/svg.image?b_x=(2\cdot\sigma(t_x)-0.5)+c_x)
![by](https://latex.codecogs.com/svg.image?b_y=(2\cdot\sigma(t_y)-0.5)+c_y)
![bw](https://latex.codecogs.com/svg.image?b_w=p_w\cdot(2\cdot\sigma(t_w))^2)
![bh](https://latex.codecogs.com/svg.image?b_h=p_h\cdot(2\cdot\sigma(t_h))^2)
Compare the center point offset before and after scaling. The center point offset range is adjusted from (0, 1) to (-0.5, 1.5).
@ -221,4 +221,4 @@ This way, the build targets process ensures that each ground truth object is pro
In conclusion, YOLOv5 represents a significant step forward in the development of real-time object detection models. By incorporating various new features, enhancements, and training strategies, it surpasses previous versions of the YOLO family in performance and efficiency.
The primary enhancements in YOLOv5 include the use of a dynamic architecture, an extensive range of data augmentation techniques, innovative training strategies, as well as important adjustments in computing losses and the process of building targets. All these innovations significantly improve the accuracy and efficiency of object detection while retaining a high degree of speed, which is the trademark of YOLO models.
The primary enhancements in YOLOv5 include the use of a dynamic architecture, an extensive range of data augmentation techniques, innovative training strategies, as well as important adjustments in computing losses and the process of building targets. All these innovations significantly improve the accuracy and efficiency of object detection while retaining a high degree of speed, which is the trademark of YOLO models.