YOLOv5 Architecture explanation

[liamh1999]

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I'm currently writing a scientific paper regarding YOLOv5 and i have questions regarding the architecture. After looking into various sources this one looks like the one most promising: #6998

According to my knowledge YOLOv5 consists of 3 main blocks: Backbone, Neck and head but where is the neck in that diagram ?. Are there any sources regarding how the workflow of the diagram works ? What are the edges in C3 block going into concat ?

Thanks in advance for helping me !

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[glenn-jocher]

@liamh1999 the YOLOv5 architecture consists of three main components: the Backbone, the Neck, and the Head. However, in the diagram you provided, the Neck component is not explicitly labeled. The diagram shows the C3 block, which is a part of the Neck in the YOLOv5 architecture.

To understand the workflow of the diagram, you can refer to the YOLOv5 source code and documentation. The architecture diagram you shared is a visual representation of the YOLOv5 model, but it may not provide detailed information about the specific operations performed within each block.

Regarding the edges in the C3 block going into concat, these edges represent the features extracted from different layers of the feature map. In the C3 block, these features are concatenated together to create a richer representation that can capture both low-level and high-level information.

For more in-depth information about the YOLOv5 architecture and its workflow, I recommend referring to the official YOLOv5 source code and the associated documentation. These resources provide detailed explanations of each component and the overall workflow of YOLOv5.

I hope this helps with your research and paper. If you have any further questions, feel free to ask. Good luck with your scientific paper!

[liamh1999]

Hi @glenn-jocher thanks for the fast reply !
After looking more into it. I found this Diagram #7160 which i think shows the split between Backbone, Neck and Head very Good ! but i'm confused with where the values come from such as I = 3 , O = 64, K = 6, S = 2 . When i look into the yolov5m.yaml https://github.com/ultralytics/yolov5/blob/master/models/yolov5m.yaml i'm not sure where these values come from as these are the values [64, 6, 2, 2] ? (for the first conv). apart from that this is my definition for c3 block and conv so far:

The conv layer is a simple convolutional layer that applies a set of filters to the input and produces an output feature map. The conv layer has four parameters: ch_in, ch_out, kernel, and stride. ch_in is the number of input channels, ch_out is the number of output channels (or filters), kernel is the size of the filter (such as 3x3), and stride is the step size of the filter (such as 2). The conv layer also has an optional parameter: padding, which is the amount of zero-padding added to the input to preserve its size. The conv layer can also have an activation function (such as ReLU) or a batch normalization layer after it.

The c3 layer is a cross-stage partial block that consists of three convolutional layers with skip connections. The c3 layer has four parameters: ch_in, ch_out, n, and e. ch_in is the number of input channels, ch_out is the number of output channels, n is the number of repetitions of the block, and e is the expansion factor. The c3 layer splits the input feature map into two parts: one part goes through a series of convolutional layers with e times ch_out channels, and the other part goes directly to the output. Then, the two parts are concatenated along the channel dimension to form the output feature map. The c3 layer can reduce the redundancy and complexity of the network by using cross-stage partial connections.

if the explanation is wrong I would be thankful for corrections.

I also want to make experiement the impact of dropout on yolov5 in regards of performance as this is part of my paper.
As far as i can see there are no dropout mechanics implemented. Is there any documentation or help of how i can implement that into the backbone ?

Thanks in advance !

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[glenn-jocher]

@liamh1999 you have a good grasp of the conv and c3 layers in the YOLOv5 architecture. The diagram you found effectively highlights the delineation between the Backbone, Neck, and Head in YOLOv5.

Regarding the values I = 3, O = 64, K = 6, and S = 2, these correspond to the input channels, output channels, kernel size, and stride of the conv layer in the YOLOv5m.yaml. These values are utilized to define the specific configuration of the convolutional layer and are essential for determining the behavior and parameters of the layer.

For evaluating the impact of dropout on YOLOv5, there isn't a built-in dropout mechanism in the current YOLOv5 implementation. To experiment with dropout in the YOLOv5 architecture, you can consider adding dropout layers into the backbone or other relevant sections of the network. You may need to modify the YOLOv5 source code to incorporate dropout layers and then conduct performance evaluations to assess its impact.

Your explanations of the conv and c3 layers are accurate, reflecting a solid understanding of these components.

Feel free to reach out if you need further assistance or clarification. Best of luck with your paper and experiments!