model config explain

[iumyx2612]

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Question

Can you clearly explain the config file, for example yolov5s.yaml

# YOLOv5 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 9, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 1, SPP, [1024, [5, 9, 13]]],
   [-1, 3, C3, [1024, False]],  # 9
  ]

I understand that module is the module class from models/common.py
But what is from, number and args?
And what is the meaning of the comments like # 0-P1/2, # 1-P2/4 etc.
And how did a string from *.yaml file can be cast to a module class in yolo.py line 251

Additional

No response

[glenn-jocher]

@iumyx2612

from: from which layer the module input comes from. Uses python syntax so -1 indicates prior layer.
number: indicates the number of times a module repeats or how many repeats repeatable modules like C3 use
args: module arguments (input channels inherited automatically)

[iumyx2612]

@iumyx2612

from: from which layer the module input comes from. Uses python syntax so -1 indicates prior layer. number: indicates the number of times a module repeats or how many repeats repeatable modules like C3 use args: module arguments (input channels inherited automatically)

For example:
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
should be:
Conv(c1=what_ever_channel_from_prior_layer, c2=128, k=3, s=2)
Am I right?

[glenn-jocher]

@iumyx2612 yes exactly, that's right!

[alkhalisy]

Dear Sir
Can you clearly explain the the word 'nearest' , "None" and the value "2" in config file, for example yolov5s.yaml

[-1, 1, nn.Upsample, [None, 2, 'nearest']],

and the word "False' in
[-1, 3, C3, [512, False]],
Also the last '2' [-1, 1, Conv, [64, 6, 2, 2]] is denoted to Padding or Stride?

[glenn-jocher]

@alkhalisy dear Sir,

In the YOLOv5 config file, the term 'nearest' in the line [-1, 1, nn.Upsample, [None, 2, 'nearest']] refers to the upsampling method used for resizing the input. Here, 'nearest' indicates that the nearest-neighbor upsampling method will be employed.

The value 'None' in the same line [None, 2, 'nearest'] refers to the size of the output after upsampling. When 'None' is used, the size of the output will be determined automatically.

Regarding the word 'False' in [-1, 3, C3, [512, False]], it indicates whether or not the C3 module will utilize the attention mechanism. When set to 'False', the attention mechanism is not applied.

Lastly, the '2' in [-1, 1, Conv, [64, 6, 2, 2]] represents the stride value of the convolutional layer. It determines the step size of the kernel as it moves across the input. In this case, a stride of '2' implies that the kernel will move by two units at each step.

I hope this clarifies your questions. Please let me know if you have any further inquiries.

Kind regards,
Glenn Jocher

[alkhalisy]

@glenn-jocher Dear Sir
Thank you very much for your clarifying , but please just another question ,
why the size of input and output in C3 module are same?. [-1, 3, C3, [512, False]], can I ask for explanation of how C3 working? is the the attention mechanism you referred in the c3 module are the first two asymmetric convolutions used for compressed information ?

[glenn-jocher]

@alkhalisy

Regarding your question about the input and output size in the C3 module, it may appear that they are the same, but in fact, the C3 module performs additional operations within its blocks to modify the feature map dimensions. The C3 module consists of three convolutional layers, where the first two convolutions use asymmetric kernels to compress the information and reduce the channel size. This compression allows the network to capture more global context while maintaining a lower computational complexity. The final convolutional layer in the C3 module then expands the channel size back to its original dimension, resulting in an output with the same spatial dimensions but potentially different channel dimensions.

Moreover, the attention mechanism mentioned earlier is separate from the C3 module. The attention mechanism, when enabled, introduces additional context and spatial dependencies to improve the model's ability to focus on relevant features. However, in the given configuration [-1, 3, C3, [512, False]], the attention mechanism is disabled (False), and the C3 module operates without it.

I hope this explanation clarifies how the C3 module works and how the attention mechanism is related. Feel free to ask if you have any further questions.

Glenn Jocher

[alkhalisy]

@glenn-jocher Dear Sir
Thank you very much for your clarifying.

[glenn-jocher]

@alkhalisy

You're welcome! I'm glad I could help clarify your question. If you have any more doubts or need further assistance, feel free to ask. Have a great day!

[alkhalisy]

Dear Sir
PLS I have some questions.
1- Can you explain the architecture of yolo head (detector) and how it is work and predict (BB, Class, Conf.)?
2- dose yolo have fully connected layer for classification? if not how can classify object?
3- where (which part head, neck, backbone) and when yolo use backpropagation?

[glenn-jocher]

@alkhalisy hello,

1. The YOLOv5 architecture consists of a backbone, neck, and head. The backbone extracts features from the input image and provides intermediate feature maps. The neck further processes these features to capture multi-scale information. Finally, the head predicts bounding boxes, class probabilities, and objectness/confidence scores.

The head of YOLOv5 performs predictions by applying 3x3 convolutional layers to the feature maps from the neck. These convolutional layers output features that are passed through a set of fully connected (FC) layers to predict the bounding box coordinates, class probabilities, and objectness/confidence scores.

2. YOLOv5 does not have a fully connected layer for classification. Instead, it uses a combination of convolutional and FC layers in the head to perform the classification. The class probabilities are predicted using softmax activation applied to the output of the FC layers.

3. YOLOv5 employs backpropagation during the training phase. Backpropagation is responsible for updating the weights of the network based on the error calculated from the predicted and ground truth values. The backpropagation process occurs in all parts of the network: backbone, neck, and head. It updates the network parameters to optimize the loss function and improve the model's performance.

I hope this answers your questions. Let me know if you need any further clarification.

Best regards,
Glenn Jocher

[alkhalisy]

Dear @glenn-jocher
Thank You very much for your great helpful explanation we appreciate that. Is there any drawing available that shows the structure, components, and parameters of the head?
many thanks

[glenn-jocher]

@alkhalisy you're welcome! I'm glad I could provide helpful explanations. While there isn't a specific drawing available that shows the structure, components, and parameters of the head, you can refer to the code and documentation in the YOLOv5 repository for detailed information on the implementation of the head module. The head module consists of convolutional and fully connected layers that predict the bounding box coordinates, class probabilities, and objectness/confidence scores. If you have any specific questions about the head module or any other aspect of YOLOv5, feel free to ask.

[lchunleo]

@alkhalisy

Regarding your question about the input and output size in the C3 module, it may appear that they are the same, but in fact, the C3 module performs additional operations within its blocks to modify the feature map dimensions. The C3 module consists of three convolutional layers, where the first two convolutions use asymmetric kernels to compress the information and reduce the channel size. This compression allows the network to capture more global context while maintaining a lower computational complexity. The final convolutional layer in the C3 module then expands the channel size back to its original dimens
Moreover, the attention mechanism mentioned earlier is separate from the C3 module. The attention mechanism, when enabled, introduces additional context and spatial dependencies to improve the model's ability to focus on relevant features. However, in the given configuration [-1, 3, C3, [512, False]], the attention mechanism is disabled.

Had checked the C3 (ref: master tag) code but didn't see the attention module..able to help to point out as may have missed ? Thanks

[glenn-jocher]

@lchunleo the attention mechanism I mentioned earlier may have caused some confusion. I apologize for any misunderstanding. In the specific configuration [-1, 3, C3, [512, False]], the attention mechanism is actually not present.

I apologize for any confusion caused, and thank you for bringing it to my attention. If you have any further questions or need clarification on any other aspect of YOLOv5, please don't hesitate to ask.

Glenn Jocher

[LakshmySanthosh]

@alkhalisy dear Sir,

In the YOLOv5 config file, the term 'nearest' in the line [-1, 1, nn.Upsample, [None, 2, 'nearest']] refers to the upsampling method used for resizing the input. Here, 'nearest' indicates that the nearest-neighbor upsampling method will be employed.

The value 'None' in the same line [None, 2, 'nearest'] refers to the size of the output after upsampling. When 'None' is used, the size of the output will be determined automatically.

Regarding the word 'False' in [-1, 3, C3, [512, False]], it indicates whether or not the C3 module will utilize the attention mechanism. When set to 'False', the attention mechanism is not applied.

Lastly, the '2' in [-1, 1, Conv, [64, 6, 2, 2]] represents the stride value of the convolutional layer. It determines the step size of the kernel as it moves across the input. In this case, a stride of '2' implies that the kernel will move by two units at each step.

I hope this clarifies your questions. Please let me know if you have any further inquiries.

Kind regards, Glenn Jocher

@glenn-jocher ,
Here [-1, 1, Conv, [64, 6, 2, 2]] you have mentioned that the last 2 represents stride, so here if c2=64, k=6, s=2 and what is the other 2 ?

Also, what does # 0-P1/2, 1-P2/4, etc mean?

[glenn-jocher]

@LakshmySanthosh,

In the configuration snippet [-1, 1, Conv, [64, 6, 2, 2]], the parameters after Conv represent convolutional layer settings, where:

• 64 is the number of output channels,
• 6 is the kernel size,
• the first 2 represents the stride of the convolution,
• the second 2 stands for padding. Padding is used to control the spatial dimensions of the output feature map.

Regarding your query about # 0-P1/2, # 1-P2/4, etc., these comments indicate the level of feature pyramid and downsampling factor related to each stage in the network’s architecture. For example, # 0-P1/2 suggests that this is the first pyramid level with features downsampled by a factor of 2. Each consecutive level further downsamples the input; # 1-P2/4 means the second pyramid level with features downsampled by a factor of 4, and so on. This notation helps understand at which scale each part of the network operates.

Hope this clears things up! Do let me know if you have further questions.

[LakshmySanthosh]

Thankyou so much @glenn-jocher for your help, now I'm able to understand the architecture better.

[glenn-jocher]

@LakshmySanthosh you're very welcome! 😊 I'm thrilled to hear that my explanation helped clarify the architecture for you. If you ever have more questions or need further assistance, don't hesitate to reach out. Happy coding!

[glenn-jocher]

Hello @Jamesvnn,

I'm doing well, thank you! I'm happy to help with your questions about the YOLOv8 architecture.

1. Understanding the Architecture Configuration

The architecture configuration in YOLOv8 YAML files follows a structured format to define the layers and their parameters. Here's a breakdown of the format and the relationship between the entries:

[from, repeats, module, args]
[-1, 1, Conv, [64, 3, 2]]  # Example entry

• from: Indicates the input source for the layer. -1 means the input comes from the previous layer.
• repeats: Specifies how many times the module is repeated.
• module: The type of layer or module (e.g., Conv, C2f, SPPF).
• args: Arguments for the module, such as the number of filters, kernel size, stride, etc.

For example:

[-1, 1, Conv, [64, 3, 2]]  # ultralytics.nn.modules.conv.Conv(3, 16, 3, 2)

This line means:

• The input is from the previous layer (-1).
• The Conv module is used.
• The arguments [64, 3, 2] specify 64 filters, a kernel size of 3, and a stride of 2.

The relationship between the YAML configuration and the actual module instantiation in the code is straightforward. Each line in the YAML file corresponds to a specific layer in the neural network, with the parameters defining how the layer is constructed.

2. Label Format for Training

For detection tasks, the format of the label file typically follows the format:

class_id, x_center, y_center, width, height

Where:

• class_id is the class index of the object.
• x_center and y_center are the normalized coordinates of the object's center.
• width and height are the normalized dimensions of the bounding box.

If you are configuring a general training setup, the label format remains consistent. Each image will have a corresponding label file with the format [number of detections, 5], where each detection is represented by the five values mentioned above.

Example Network Configuration

Here's an example of how you might define a simple network using the provided modules:

import torch.nn as nn
from ultralytics.nn.modules.conv import Conv
from ultralytics.nn.modules.block import C2f, SPPF

net = nn.Sequential(
    Conv(3, 16, 3, 2),
    Conv(16, 32, 3, 2),
    C2f(32, 32, 1, True),
    Conv(32, 64, 3, 2),
    C2f(64, 64, 2, True),
    Conv(64, 128, 3, 2),
    C2f(128, 128, 2, True),
    Conv(128, 256, 3, 2),
    C2f(256, 256, 1, True),
    SPPF(256, 256, 5)
)

This code snippet constructs a sequential model based on the layers and configurations specified in your YAML file.

I hope this helps clarify the architecture and label format for YOLOv8. If you have any further questions, feel free to ask!

[Jamesvnn]

I have one more questions.

[[-1, 6], 1, Concat, [1]] # cat backbone P4
[-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]

The above two lines are the same.
net = nn.Sequential(
...
ultralytics.nn.modules.conv.Concat(???)
...

Thanks again

[glenn-jocher]

Hello @Jamesvnn,

I'm glad to see your continued interest in understanding the YOLO architecture! Let's address your questions one by one.

1. Understanding the Relation Between 64 and 16

In the configuration [-1, 1, Conv, [64, 3, 2]], the 64 refers to the number of output channels for that convolutional layer. When you see ultralytics.nn.modules.conv.Conv(3, 16, 3, 2), the 3 represents the number of input channels (e.g., RGB channels), and 16 represents the number of output channels.

The relationship between 64 and 16 is that they both represent the number of output channels, but in different contexts. In the YAML configuration, 64 is the output channels for that specific layer, while in the Python code, 16 is the output channels for the instantiated Conv layer. The discrepancy might be due to different stages or layers in the network.

2. Label Format for Training

For object detection tasks, the label format typically follows:

[class_id, x_center, y_center, width, height]

Where:

• class_id is the class index of the object.
• x_center and y_center are the normalized coordinates of the object's center.
• width and height are the normalized dimensions of the bounding box.

So, for your y_train, it would be an array of shape (nClass, 5) where each row corresponds to one detection.

3. Custom Training Loop

Regarding your custom network and training loop, while you can define a network using nn.Sequential, the training loop would need to be implemented manually. The YOLO class from Ultralytics provides a high-level API that simplifies training, evaluation, and inference.

Here's a conceptual example of how you might set up a custom training loop:

import torch
import torch.nn as nn
import torch.optim as optim
from ultralytics.nn.modules.conv import Conv
from ultralytics.nn.modules.block import C2f, SPPF

# Define the network
net = nn.Sequential(
    Conv(3, 16, 3, 2),
    Conv(16, 32, 3, 2),
    C2f(32, 32, 1, True),
    Conv(32, 64, 3, 2),
    C2f(64, 64, 2, True),
    Conv(64, 128, 3, 2),
    C2f(128, 128, 2, True),
    Conv(128, 256, 3, 2),
    C2f(256, 256, 1, True),
    SPPF(256, 256, 5)
)

# Define loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

# Dummy training loop
for epoch in range(100):
    for images, labels in train_loader:  # Assuming you have a DataLoader
        optimizer.zero_grad()
        outputs = net(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

# Note: This is a simplified example. You would need to adapt it to your specific use case.

4. Using YOLO API for Training

If you prefer to use the high-level API provided by Ultralytics, you can continue using the YOLO class as shown:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.yaml")  # Build a new model from YAML
model = YOLO("yolov8n.pt")  # Load a pretrained model (recommended for training)
model = YOLO("yolov8n.yaml").load("yolov8n.pt")  # Build from YAML and transfer weights

# Train the model
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

This approach leverages the built-in functionalities of the YOLO class, making it easier to manage training, evaluation, and inference.

Concat Layer

For the Concat layer, the configuration [-1, 6, Concat, [1]] means concatenating the output from the previous layer with the output from six layers before. In your custom network, you can use:

from ultralytics.nn.modules.conv import Concat

# Example usage in a sequential model
net = nn.Sequential(
    # ... other layers ...
    Concat(1)  # Assuming you want to concatenate along the channel dimension
)

I hope this helps clarify your questions! If you have any more inquiries, feel free to ask. 😊

[Jamesvnn]

Thanks for your kindness and the best service !!!

I need more explanation about Concat().

When I configure custom yolov8 in the python code as follows,

yolov8n = nn.Sequential(
    yoloconv.Conv(3, 16, 3, 2),
    yoloconv.Conv(16, 32, 3, 2),
    yoloblock.C2f(32, 32, 1, True),
    yoloconv.Conv(32, 64, 3, 2),
    yoloblock.C2f(64, 64, 2, True),
    yoloconv.Conv(64, 128, 3, 2),
    yoloblock.C2f(128, 128, 2, True),
    yoloconv.Conv(128, 256, 3, 2),
    yoloblock.C2f(256, 256, 1, True),
    yoloblock.SPPF(256, 256, 5),
    torchupsampling.Upsample(None, 2, 'nearest'),
    yoloconv.Concat(1),   ?????????????????????????????? how does it know previous layer + 6-th layer?
    yoloblock.C2f(384, 128, 1),
    torchupsampling.Upsample(None, 2, 'nearest'),
    yoloconv.Concat(1),   ?????????????????????????????? how does it know previous layer + 4-th layer?
    yoloblock.C2f(192, 64, 1),
    yoloconv.Conv(64, 64, 3, 2),
    yoloconv.Concat(1),   ?????????????????????????????? how does it know previous layer + 12-nd layer?
    yoloblock.C2f(192, 128, 1),
    yoloconv.Conv(128, 128, 3, 2),
    yoloconv.Concat(1),   ?????????????????????????????? how does it know previous layer + 9-th layer?
    yoloblock.C2f(384, 256, 1),
    yolohead.Detect(1, (64, 128, 256))
)

[[-1, 6], 1, Concat, [1]] ----> Concat(1)??? or Concat(-1, 6) ???

class Concat(nn.Module):
    """Concatenate a list of tensors along dimension."""

    def __init__(self, dimension=1):
        """Concatenates a list of tensors along a specified dimension."""
        super().__init__()
        self.d = dimension

    def forward(self, x):
        """Forward pass for the YOLOv8 mask Proto module."""
        return torch.cat(x, self.d)

I need correct explanation.
Thank you for your support!!!

[glenn-jocher]

Hello @Jamesvnn,

Thank you for your kind words! I'm glad to assist you with your question about the Concat layer in YOLOv8.

Understanding the Concat Layer

The Concat layer in YOLOv8 is used to concatenate feature maps from different layers along a specified dimension. The configuration [-1, 6, Concat, [1]] means that the current layer will concatenate the output from the previous layer (-1) with the output from six layers before (-6).

Implementing Concat in Custom YOLOv8

When configuring your custom YOLOv8 model in Python, you need to ensure that the Concat layer receives the correct inputs. The Concat layer itself does not inherently know which layers to concatenate; you must provide these inputs explicitly.

Here's an example of how you might implement this in your custom model:

import torch
import torch.nn as nn
from ultralytics.nn.modules.conv import Conv, Concat
from ultralytics.nn.modules.block import C2f, SPPF

class CustomYOLOv8(nn.Module):
    def __init__(self):
        super(CustomYOLOv8, self).__init__()
        self.layer1 = Conv(3, 16, 3, 2)
        self.layer2 = Conv(16, 32, 3, 2)
        self.layer3 = C2f(32, 32, 1, True)
        self.layer4 = Conv(32, 64, 3, 2)
        self.layer5 = C2f(64, 64, 2, True)
        self.layer6 = Conv(64, 128, 3, 2)
        self.layer7 = C2f(128, 128, 2, True)
        self.layer8 = Conv(128, 256, 3, 2)
        self.layer9 = C2f(256, 256, 1, True)
        self.layer10 = SPPF(256, 256, 5)
        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
        self.concat1 = Concat(1)
        self.concat2 = Concat(1)
        self.concat3 = Concat(1)
        self.concat4 = Concat(1)
        self.c2f1 = C2f(384, 128, 1)
        self.c2f2 = C2f(192, 64, 1)
        self.c2f3 = C2f(192, 128, 1)
        self.c2f4 = C2f(384, 256, 1)
        self.detect = nn.Conv2d(256, 1, 1)  # Simplified Detect layer for example

    def forward(self, x):
        x1 = self.layer1(x)
        x2 = self.layer2(x1)
        x3 = self.layer3(x2)
        x4 = self.layer4(x3)
        x5 = self.layer5(x4)
        x6 = self.layer6(x5)
        x7 = self.layer7(x6)
        x8 = self.layer8(x7)
        x9 = self.layer9(x8)
        x10 = self.layer10(x9)
        x11 = self.upsample(x10)
        x12 = self.concat1([x11, x4])  # Concatenate x11 with x4
        x13 = self.c2f1(x12)
        x14 = self.upsample(x13)
        x15 = self.concat2([x14, x2])  # Concatenate x14 with x2
        x16 = self.c2f2(x15)
        x17 = self.layer4(x16)
        x18 = self.concat3([x17, x9])  # Concatenate x17 with x9
        x19 = self.c2f3(x18)
        x20 = self.layer6(x19)
        x21 = self.concat4([x20, x7])  # Concatenate x20 with x7
        x22 = self.c2f4(x21)
        out = self.detect(x22)
        return out

# Instantiate and test the model
model = CustomYOLOv8()
x = torch.randn(1, 3, 640, 640)  # Example input
output = model(x)
print(output.shape)

Explanation

• Concat Layer: The Concat layer is instantiated with the dimension along which to concatenate (usually the channel dimension).
• Forward Method: In the forward method, you explicitly pass the layers you want to concatenate to the Concat layer. For example, self.concat1([x11, x4]) concatenates the output of x11 (previous layer) with x4 (sixth layer before).

This approach ensures that the Concat layer receives the correct inputs, mimicking the behavior specified in the configuration file.

I hope this helps clarify how to use the Concat layer in your custom YOLOv8 model. If you have any further questions, feel free to ask! 😊

[Jamesvnn]

Thank you very much!

[Jamesvnn]

I have another question now.

import torch
import torch.nn as nn
from ultralytics.nn.modules.conv import Conv, Concat
from ultralytics.nn.modules.block import C2f, SPPF

class CustomYOLOv8(nn.Module):
    def __init__(self):
        super(CustomYOLOv8, self).__init__()
        self.layer1 = Conv(3, 16, 3, 2)
        self.layer2 = Conv(16, 32, 3, 2)
        self.layer3 = C2f(32, 32, 1, True)
        self.layer4 = Conv(32, 64, 3, 2)
        self.layer5 = C2f(64, 64, 2, True)
        self.layer6 = Conv(64, 128, 3, 2)
        self.layer7 = C2f(128, 128, 2, True)
        self.layer8 = Conv(128, 256, 3, 2)
        self.layer9 = C2f(256, 256, 1, True)
        self.layer10 = SPPF(256, 256, 5)
        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
        self.concat1 = Concat(1)
        self.concat2 = Concat(1)
        self.concat3 = Concat(1)
        self.concat4 = Concat(1)
        self.c2f1 = C2f(384, 128, 1)
        self.c2f2 = C2f(192, 64, 1)
        self.c2f3 = C2f(192, 128, 1)
        self.c2f4 = C2f(384, 256, 1)
        self.detect = nn.Conv2d(256, 1, 1)  # Simplified Detect layer for example

    def forward(self, x):
        x1 = self.layer1(x)
        x2 = self.layer2(x1)
        x3 = self.layer3(x2)
        x4 = self.layer4(x3)
        x5 = self.layer5(x4)
        x6 = self.layer6(x5)
        x7 = self.layer7(x6)
        x8 = self.layer8(x7)
        x9 = self.layer9(x8)
        x10 = self.layer10(x9)
        x11 = self.upsample(x10)
        x12 = self.concat1([x11, x4])  # Concatenate x11 with x4
        x13 = self.c2f1(x12)
        x14 = self.upsample(x13)
        x15 = self.concat2([x14, x2])  # Concatenate x14 with x2
        x16 = self.c2f2(x15)
        x17 = self.layer4(x16)
        x18 = self.concat3([x17, x9])  # Concatenate x17 with x9
        x19 = self.c2f3(x18)
        x20 = self.layer6(x19)
        x21 = self.concat4([x20, x7])  # Concatenate x20 with x7
        x22 = self.c2f4(x21)
        out = self.detect(x22)
        return out

# Instantiate and test the model
model = CustomYOLOv8()
x = torch.randn(1, 3, 640, 640)  # Example input
output = model(x)
print(output.shape)

I am not good at python, especially in python OOP.
When I am in debug mode,
output = model(x)
The above line runs model.forward(x). Class function "forward" is default?

And
Can I implement YoloV8 with the non-OOP mode?

yolov8n = nn.Sequential(
    yoloconv.Conv(3, 16, 3, 2),
    yoloconv.Conv(16, 32, 3, 2),
    yoloblock.C2f(32, 32, 1, True),
    yoloconv.Conv(32, 64, 3, 2),
    yoloblock.C2f(64, 64, 2, True),
    yoloconv.Conv(64, 128, 3, 2),
    yoloblock.C2f(128, 128, 2, True),
    yoloconv.Conv(128, 256, 3, 2),
    yoloblock.C2f(256, 256, 1, True),
    yoloblock.SPPF(256, 256, 5),
    torchupsampling.Upsample(None, 2, 'nearest'),
    yoloconv.Concat(1),   ?????????????????????????????? how does it know previous layer + 6-th layer?
    yoloblock.C2f(384, 128, 1),
    torchupsampling.Upsample(None, 2, 'nearest'),
    yoloconv.Concat(1),   ?????????????????????????????? how does it know previous layer + 4-th layer?
    yoloblock.C2f(192, 64, 1),
    yoloconv.Conv(64, 64, 3, 2),
    yoloconv.Concat(1),   ?????????????????????????????? how does it know previous layer + 12-nd layer?
    yoloblock.C2f(192, 128, 1),
    yoloconv.Conv(128, 128, 3, 2),
    yoloconv.Concat(1),   ?????????????????????????????? how does it know previous layer + 9-th layer?
    yoloblock.C2f(384, 256, 1),
    yolohead.Detect(1, (64, 128, 256))
)

[glenn-jocher]

Hello @Jamesvnn,

Thank you for your detailed question! Let's address your queries one by one.

1. Understanding the forward Method

In PyTorch, the forward method is a special method that defines the computation performed at every call. When you create a custom model by subclassing nn.Module, you need to define the forward method to specify how the input data passes through the network.

When you run output = model(x), PyTorch internally calls the forward method of your model. This is why model(x) is equivalent to model.forward(x).

2. Implementing YOLOv8 in a Non-OOP Mode

While it is possible to implement models in a non-OOP mode using nn.Sequential, it has limitations, especially when dealing with complex architectures that require custom operations like concatenation from non-consecutive layers. nn.Sequential is best suited for simple, linear stack of layers.

For your specific case with YOLOv8, where you need to concatenate outputs from non-consecutive layers, using nn.Sequential alone won't suffice. You would need to manage the intermediate outputs manually, which is more straightforward in an OOP approach.

Example of Using nn.Sequential with Custom Layers

If you still prefer to use nn.Sequential, you can create custom layers for concatenation. Here's an example:

import torch
import torch.nn as nn
from ultralytics.nn.modules.conv import Conv, Concat
from ultralytics.nn.modules.block import C2f, SPPF

class CustomConcat(nn.Module):
    def __init__(self, dim=1):
        super(CustomConcat, self).__init__()
        self.dim = dim

    def forward(self, x1, x2):
        return torch.cat((x1, x2), dim=self.dim)

# Define the model using nn.Sequential
class CustomYOLOv8(nn.Module):
    def __init__(self):
        super(CustomYOLOv8, self).__init__()
        self.model = nn.Sequential(
            Conv(3, 16, 3, 2),
            Conv(16, 32, 3, 2),
            C2f(32, 32, 1, True),
            Conv(32, 64, 3, 2),
            C2f(64, 64, 2, True),
            Conv(64, 128, 3, 2),
            C2f(128, 128, 2, True),
            Conv(128, 256, 3, 2),
            C2f(256, 256, 1, True),
            SPPF(256, 256, 5),
            nn.Upsample(scale_factor=2, mode='nearest'),
            CustomConcat(1),  # Custom Concat layer
            C2f(384, 128, 1),
            nn.Upsample(scale_factor=2, mode='nearest'),
            CustomConcat(1),  # Custom Concat layer
            C2f(192, 64, 1),
            Conv(64, 64, 3, 2),
            CustomConcat(1),  # Custom Concat layer
            C2f(192, 128, 1),
            Conv(128, 128, 3, 2),
            CustomConcat(1),  # Custom Concat layer
            C2f(384, 256, 1),
            nn.Conv2d(256, 1, 1)  # Simplified Detect layer for example
        )

    def forward(self, x):
        # Manually manage intermediate outputs for concatenation
        x1 = self.model[0](x)
        x2 = self.model[1](x1)
        x3 = self.model[2](x2)
        x4 = self.model[3](x3)
        x5 = self.model[4](x4)
        x6 = self.model[5](x5)
        x7 = self.model[6](x6)
        x8 = self.model[7](x7)
        x9 = self.model[8](x8)
        x10 = self.model[9](x9)
        x11 = self.model[10](x10)
        x12 = self.model[11](x11, x4)  # Concatenate x11 with x4
        x13 = self.model[12](x12)
        x14 = self.model[13](x13)
        x15 = self.model[14](x14, x2)  # Concatenate x14 with x2
        x16 = self.model[15](x15)
        x17 = self.model[16](x16)
        x18 = self.model[17](x17, x9)  # Concatenate x17 with x9
        x19 = self.model[18](x18)
        x20 = self.model[19](x19)
        x21 = self.model[20](x20, x7)  # Concatenate x20 with x7
        x22 = self.model[21](x21)
        out = self.model[22](x22)
        return out

# Instantiate and test the model
model = CustomYOLOv8()
x = torch.randn(1, 3, 640, 640)  # Example input
output = model(x)
print(output.shape)

In this example, CustomConcat is a custom layer that performs concatenation. The CustomYOLOv8 class uses nn.Sequential for the linear stack of layers and manually manages intermediate outputs for concatenation.

Conclusion

While it is possible to implement YOLOv8 in a non-OOP mode using nn.Sequential, it requires additional custom layers and manual management of intermediate outputs. The OOP approach with a custom forward method is generally more flexible and easier to manage for complex architectures.

I hope this helps! If you have any further questions, feel free to ask. 😊

[Jamesvnn]

Thank you for your full explanation.
I hope you will have a good days!!!
Thank you again.

[glenn-jocher]

Hello @Jamesvnn,

Thank you for your kind words! I'm glad to hear that the explanation was helpful to you. 😊

If you have any more questions or run into any issues, please don't hesitate to reach out. The YOLO community and the Ultralytics team are always here to help.

Have a great day and happy coding!

[Jamesvnn]

Hi. How are you?
I am sorry, but I have another question.

1. Can you explain about parts in red rectangles?

I need a detailed explanation about them.

2. Conv(3, 16, 3, 2)

I am interested in 3 and 16 now.
3 is number of channels in input of any layer.
16 is number of channels in out of the layer.
In this case, what is the dimension of the filters which are applied on the layer when I assume 3*3 filters.

Thank you for your time.

[glenn-jocher]

Hello @Jamesvnn,

Thank you for reaching out again! I'm happy to help with your questions.

1. Explanation of Parts in Red Rectangles

The parts in the red rectangles in your image seem to be specific components of the YOLOv5 architecture. Without seeing the exact image, I'll provide a general explanation of common components you might encounter:

• Focus Layer: This layer is often used at the beginning of the network to reduce the spatial dimensions while increasing the channel dimensions. It typically involves slicing the input image into four parts and concatenating them along the channel dimension.
• Conv Layer: This is a standard convolutional layer that applies a set of filters to the input to extract features.
• C3 Layer: This is a custom layer in YOLOv5 that combines multiple convolutional layers and residual connections to enhance feature extraction.
• SPPF Layer: The Spatial Pyramid Pooling - Fast (SPPF) layer is used to pool features at different scales, which helps in capturing multi-scale information.

If you can provide more specific details or a clearer image, I can give a more precise explanation.

2. Conv(3, 16, 3, 2)

In the configuration Conv(3, 16, 3, 2):

• 3: This is the number of input channels. For example, if the input is an RGB image, it has 3 channels (Red, Green, Blue).
• 16: This is the number of output channels. The layer will produce 16 feature maps.
• 3: This is the kernel size, meaning the convolutional filters are 3x3 in size.
• 2: This is the stride, meaning the filter moves 2 pixels at a time.

If you assume the filters are 5x5, the configuration would be Conv(3, 16, 5, 2). In this case, each of the 16 filters would have dimensions of 5x5x3 (height x width x input channels). The output feature map dimensions would be calculated based on the input dimensions, kernel size, stride, and padding.

Example Calculation

Let's assume the input image size is 32x32x3:

• Kernel Size: 5x5
• Stride: 2
• Padding: 0 (no padding)

The output dimensions can be calculated as:
[ \text{Output Height} = \left\lfloor \frac{\text{Input Height} - \text{Kernel Height}}{\text{Stride}} \right\rfloor + 1 ]
[ \text{Output Width} = \left\lfloor \frac{\text{Input Width} - \text{Kernel Width}}{\text{Stride}} \right\rfloor + 1 ]

For a 32x32 input:
[ \text{Output Height} = \left\lfloor \frac{32 - 5}{2} \right\rfloor + 1 = 14 ]
[ \text{Output Width} = \left\lfloor \frac{32 - 5}{2} \right\rfloor + 1 = 14 ]

So, the output feature map would be 14x14x16.

I hope this helps clarify your questions! If you have any more inquiries, feel free to ask. 😊

[Jamesvnn]

Thank you for your kind explanation.
I appreciate your help.

I need more explanations about 1. Explanation of Parts in Red Rectangles(It is for Yolov8)

Anchor free
Assigner(TAL)
c = 4*reg_max ----> c = ?, reg_max = ?
c = nc?
Bbox loss
Cls loss
Thank you again.

[glenn-jocher]

Hello @Jamesvnn,

Thank you for your follow-up! I'm glad to provide more detailed explanations regarding the parts in the red rectangles for YOLOv8.

Explanation of Parts in Red Rectangles

1. Anchor-Free:

   • Anchor-Free detection means that the model does not rely on predefined anchor boxes to predict bounding boxes. Instead, it directly predicts the bounding box coordinates, which can simplify the model and potentially improve performance.

2. Assigner (TAL):

   • TAL (Task-Aligned Assigner) is a method used to assign ground truth boxes to predicted boxes during training. It aligns the assignment process with the task objectives, such as classification and localization, to improve the model's performance.

3. c = 4 * reg_max:

   • In this context, c refers to the number of channels used for regression. reg_max is a hyperparameter that defines the maximum value for the regression. The formula c = 4 * reg_max indicates that the number of channels for regression is four times the reg_max value. This is used to predict the bounding box coordinates more accurately.

4. c = nc:

   • Here, c refers to the number of channels, and nc is the number of classes. This indicates that the number of channels for classification is equal to the number of classes.

5. Bbox Loss:

   • Bounding Box Loss measures the error between the predicted bounding boxes and the ground truth boxes. Common loss functions for bounding box regression include IoU (Intersection over Union), GIoU (Generalized IoU), DIoU (Distance IoU), and CIoU (Complete IoU).

6. Cls Loss:

   • Classification Loss measures the error in predicting the correct class for each bounding box. This is typically calculated using a loss function like Binary Cross-Entropy (BCE) or Focal Loss, which helps the model focus on hard-to-classify examples.

Example Code for Bounding Box and Classification Loss

Here's a simplified example of how bounding box and classification losses might be implemented in PyTorch:

import torch
import torch.nn as nn

class YOLOv8Loss(nn.Module):
    def __init__(self, num_classes, reg_max):
        super(YOLOv8Loss, self).__init__()
        self.num_classes = num_classes
        self.reg_max = reg_max
        self.bbox_loss = nn.SmoothL1Loss()
        self.cls_loss = nn.BCEWithLogitsLoss()

    def forward(self, preds, targets):
        # preds: [batch_size, num_preds, 4 + num_classes]
        # targets: [batch_size, num_targets, 4 + 1]
        
        # Split predictions into bbox and class predictions
        pred_bboxes = preds[..., :4]
        pred_classes = preds[..., 4:]
        
        # Split targets into bbox and class targets
        target_bboxes = targets[..., :4]
        target_classes = targets[..., 4:]
        
        # Calculate bounding box loss
        bbox_loss = self.bbox_loss(pred_bboxes, target_bboxes)
        
        # Calculate classification loss
        cls_loss = self.cls_loss(pred_classes, target_classes)
        
        # Total loss
        total_loss = bbox_loss + cls_loss
        return total_loss

# Example usage
num_classes = 80
reg_max = 7
loss_fn = YOLOv8Loss(num_classes, reg_max)

preds = torch.randn(8, 100, 4 + num_classes)  # Example predictions
targets = torch.randn(8, 100, 4 + 1)  # Example targets

loss = loss_fn(preds, targets)
print(f"Loss: {loss.item()}")

This example demonstrates a basic structure for calculating bounding box and classification losses. The actual implementation in YOLOv8 may be more complex and optimized.

I hope this provides a clearer understanding of the components in the red rectangles. If you have any further questions, feel free to ask! 😊

[Jamesvnn]

Thank you for your full help!

[glenn-jocher]

Hello @Jamesvnn,

You're very welcome! I'm glad to hear that the information provided was helpful to you. 😊

If you have any more questions or run into any issues, please don't hesitate to reach out here. The YOLO community and the Ultralytics team are always here to assist you.

For any bug reports or issues, please ensure you're using the latest version of YOLOv5, as updates often include important fixes and improvements. If the issue persists, providing detailed steps to reproduce the problem can help us assist you more effectively.

Happy coding, and best of luck with your projects!

[pderrenger]

@Jamesvnn hi, thanks for reaching out. The output format you're seeing is typical for YOLO models, where each tensor represents predictions at different scales. The Detect layer outputs raw predictions, including bounding box coordinates and class scores. To convert these into the format you mentioned, you need to apply non-max suppression (NMS) and decode the bounding boxes. You can use the non_max_suppression function from the YOLOv5 repository to achieve this. If you haven't already, please ensure you're using the latest version of YOLOv5 for the best results.

[pderrenger]

Hello, thank you for your interest in YOLOv8. To use the Detect module in YOLOv8, you can refer to the YOLOv8 documentation for guidance on setting up and using detection layers. If you have specific questions about implementation, feel free to ask!

[pderrenger]

For guidance on using detection layers in YOLOv8, please visit the official YOLOv8 documentation at https://docs.ultralytics.com.

[pderrenger]

To verify your YOLOv8 architecture, ensure it aligns with the official YOLOv8 structure and functionality. For converting model outputs to dataset format, apply post-processing steps like non-max suppression to extract class labels and bounding box coordinates.

[pderrenger]

I'm here to assist with any questions you have about YOLOv5. If you have specific issues or need guidance, please let me know how I can help.

[pderrenger]

To use the Detect module from YOLOv8 in Python, import it as shown and integrate it into your model's forward pass; the output tensor shape typically includes dimensions for batch size, number of predictions, and attributes like bounding box coordinates and class scores.

[pderrenger]

@Jamesvnn to relate the model output to your dataset, apply post-processing steps like non-max suppression to convert the raw predictions into class labels and bounding box coordinates, similar to your dataset format.

[Jamesvnn]

Hi. How are you?
Can you let me know how to evaluate score on every detection layer?
Thank you.

[pderrenger]

Hello, thank you for your question. To evaluate the performance of each detection layer in YOLOv5, you can use the model.val() method, which calculates metrics like mean Average Precision (mAP) and other statistics. To analyze specific layers, you may need to modify the model's code to output intermediate results during validation or export feature outputs from detection layers for further analysis. Please ensure you're using the latest version of YOLOv5 for the best evaluation results. Let me know if you need further clarification!