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YOLOv5 is a deep learning-based target detection algorithm and the latest version of the YOLO series of algorithms. YOLO is the abbreviation of You Only Look Once, which means that only one forward pass is required to complete the target detection task, so it has a very fast detection speed and high accuracy.
Compared with YOLOv4, YOLOv5 has been improved and optimized in many aspects, including faster training speed, higher accuracy, smaller model size, etc. The following are some features of YOLOv5:
faster training
YOLOv5 adopts a new training method called Scaled-YOLOv4, which speeds up the training speed of the model without reducing the accuracy of the model. In addition, YOLOv5 also adopts a new data enhancement method called Mosaic Data Augmentation, which can improve the robustness and generalization ability of the model.
higher precision
YOLOv5 has been optimized in the model structure and training process, which has significantly improved the accuracy of the model. In addition, YOLOv5 also adopts a new test method called Ensemble Anchors, which can further improve the accuracy of the model.
smaller model size
YOLOv5 has successfully achieved lightweight models through a series of lightweight techniques, such as network pruning, feature pyramid structure, network width and depth, lightweight regularization, lightweight activation functions, and knowledge distillation. This makes the model volume of YOLOv5 significantly compressed, which can better adapt to different scenes and devices.
More application scenarios
YOLOv5 can be applied to many different scenarios, such as intelligent transportation, intelligent security, intelligent agriculture, unmanned driving, robots, etc. In addition, YOLOv5 can also adapt to different devices, such as CPU, GPU, TPU, etc., and can achieve efficient target detection on different hardware platforms.
In general, YOLOv5 is a very good target detection algorithm with faster training speed, higher accuracy, smaller model size and wider application scenarios, which can bring more value to practical applications.
In order to make the model more lightweight, Yolov5 adopts a series of effective technologies, mainly including the following aspects:
convolution kernel pruning
Convolution kernel pruning is a commonly used lightweight technique, which reduces the number of model parameters by removing unimportant convolution kernels. In Yolov5, pruning algorithm can be used to delete some unimportant convolution kernels. Specifically, the Taylor Expansion method can be used to evaluate the importance of convolution kernels, and then delete some less important convolution kernels, thereby reducing the size of the model. In addition, dynamic pruning technology can also be used to dynamically prune some convolution kernels according to specific situations to adapt to different scenarios and devices.
Feature Pyramid Structure
Feature pyramid structure is a commonly used image processing technique, which can extract feature images of different sizes, so as to adapt to different scenes and targets. In Yolov5, a special feature pyramid structure is adopted, which can reduce the size of the model and the amount of calculation while maintaining high precision. Specifically, Yolov5 uses a CSP (cross-stage partial) structure, which can reduce the depth and calculation of the network by splicing feature maps of different levels. In addition, the CSP structure can also effectively improve the accuracy and speed of the model.
Network Width and Depth
The width and depth of the network are important factors affecting model size and performance. In Yolov5, the size and computation of the model can be reduced by adjusting the network width and depth. Different network structures can be adopted to adapt to different scenarios and devices. For example, a shallow network can be used for low-end devices, and a deep network can be used for high-end devices. In addition, different convolution kernel sizes and strides can be used to adjust the network width and depth.
lightweight regularization
Regularization is a technique used to control model complexity and avoid overfitting. In Yolov5, lightweight regularization techniques can be used to reduce the size and computation of the model. Specifically, lightweight regularization methods such as Group Normalization (GN) can be used to control model complexity and improve model accuracy and speed. GN can reduce the amount of memory and calculation required by Batch Normalization (BN), and can use a large batch size during training, thereby improving the training efficiency of the model.
activation function
The activation function is a very important part of the neural network, which can introduce nonlinear factors into the model and improve the expressive ability of the model. In Yolov5, the model size and computation can be reduced by using lightweight activation functions. Specifically, an activation function such as Mish can be used instead of the commonly used ReLU activation function. The Mish activation function can not only improve the accuracy and speed of the model, but also reduce the size and computation of the model.
knowledge distillation
Knowledge distillation is an effective model compression technique, which can reduce the size of the model and the amount of computation by transferring the knowledge of a large model to a small model. In Yolov5, knowledge distillation technology can be used to reduce the size of the model and the amount of computation. Specifically, the knowledge of a large Yolov5 model can be transferred to a small Yolov5 model to obtain a lightweight Yolov5 model.
In general, Yolov5 has successfully realized the lightweight of the model through a series of effective lightweight techniques, such as convolution kernel pruning, feature pyramid structure, network width and depth, lightweight regularization, lightweight activation functions, and knowledge distillation. This enables Yolov5 to adapt to different scenarios and devices, improving the performance and efficiency of the model.
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