Modifying YOLOv5 model for a common backbone but "2 different heads"

[jain-abhay]

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Question

Hi, I was curious to know that if I wish to modify the YOLOv5 model, for a new model that has a common backbone but 2 different heads, then which all files require changes/modifications?
In my knowledge, I guess the yaml file will have 2 heads defined now and then we might have to change common.py and yolo.py
But please I would really appreciate if you give a detailed clarity about what all needs to be modified in these files. Thanks in anticipation.

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[glenn-jocher]

@jain-abhay hello,

Thank you for your question and for searching the existing issues and discussions before posting. Modifying the YOLOv5 model to include a common backbone with two different heads is an interesting task and can be quite useful for various applications.

To achieve this, you'll need to make changes in several files. Here's a detailed guide to help you through the process:

1. Model Configuration (YAML) File:

   • You'll need to define the two heads in your model configuration YAML file. This involves specifying the architecture of each head and how they connect to the common backbone.

2. common.py:

   • This file contains common layers and blocks used in YOLOv5. Depending on the complexity of your heads, you might need to add or modify some layers here. For instance, if your heads require unique layers not present in the current implementation, you'll define them here.

3. yolo.py:

   • This file is crucial as it defines the YOLO model. You'll need to modify the Model class to accommodate the two heads. Specifically, you'll:
     • Adjust the forward pass to handle the outputs from both heads.
     • Ensure that the loss computation and other relevant processes are correctly applied to both heads.

Here's a simplified example to give you an idea of what changes might look like in yolo.py:

class Model(nn.Module):
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):
        super(Model, self).__init__()
        # Load model configuration
        self.yaml = self._load_yaml(cfg)
        # Define the backbone
        self.backbone = self._build_backbone(self.yaml['backbone'])
        # Define the two heads
        self.head1 = self._build_head(self.yaml['head1'])
        self.head2 = self._build_head(self.yaml['head2'])

    def forward(self, x):
        x = self.backbone(x)
        out1 = self.head1(x)
        out2 = self.head2(x)
        return out1, out2

    def _build_backbone(self, backbone_cfg):
        # Build the backbone from the configuration
        pass

    def _build_head(self, head_cfg):
        # Build the head from the configuration
        pass

4. Loss Computation:

   • If the two heads are performing different tasks (e.g., object detection and segmentation), you'll need to ensure that the loss functions for each head are correctly implemented and combined during training.

5. Training Script:

   • Update the training script to handle the outputs from both heads. This includes modifying the loss calculation and backpropagation steps to accommodate the dual-head architecture.

For more detailed information on the YOLOv5 architecture and how to modify it, you can refer to the YOLOv5 Architecture Description.

If you encounter any specific issues or need further assistance, please provide a minimum reproducible example of your code. This will help us better understand the problem and provide more accurate guidance. You can find more information on creating a minimum reproducible example here.

Best of luck with your modifications! If you have any more questions, feel free to ask.

[jain-abhay]

@jain-abhay hello,

Thank you for your question and for searching the existing issues and discussions before posting. Modifying the YOLOv5 model to include a common backbone with two different heads is an interesting task and can be quite useful for various applications.

To achieve this, you'll need to make changes in several files. Here's a detailed guide to help you through the process:

1. Model Configuration (YAML) File:

   • You'll need to define the two heads in your model configuration YAML file. This involves specifying the architecture of each head and how they connect to the common backbone.

2. common.py:

   • This file contains common layers and blocks used in YOLOv5. Depending on the complexity of your heads, you might need to add or modify some layers here. For instance, if your heads require unique layers not present in the current implementation, you'll define them here.

3. yolo.py:

   • This file is crucial as it defines the YOLO model. You'll need to modify the Model class to accommodate the two heads. Specifically, you'll:

     • Adjust the forward pass to handle the outputs from both heads.
     • Ensure that the loss computation and other relevant processes are correctly applied to both heads.

Here's a simplified example to give you an idea of what changes might look like in yolo.py:

class Model(nn.Module):
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):
        super(Model, self).__init__()
        # Load model configuration
        self.yaml = self._load_yaml(cfg)
        # Define the backbone
        self.backbone = self._build_backbone(self.yaml['backbone'])
        # Define the two heads
        self.head1 = self._build_head(self.yaml['head1'])
        self.head2 = self._build_head(self.yaml['head2'])

    def forward(self, x):
        x = self.backbone(x)
        out1 = self.head1(x)
        out2 = self.head2(x)
        return out1, out2

    def _build_backbone(self, backbone_cfg):
        # Build the backbone from the configuration
        pass

    def _build_head(self, head_cfg):
        # Build the head from the configuration
        pass

4. Loss Computation:

   • If the two heads are performing different tasks (e.g., object detection and segmentation), you'll need to ensure that the loss functions for each head are correctly implemented and combined during training.

5. Training Script:

   • Update the training script to handle the outputs from both heads. This includes modifying the loss calculation and backpropagation steps to accommodate the dual-head architecture.

For more detailed information on the YOLOv5 architecture and how to modify it, you can refer to the YOLOv5 Architecture Description.

If you encounter any specific issues or need further assistance, please provide a minimum reproducible example of your code. This will help us better understand the problem and provide more accurate guidance. You can find more information on creating a minimum reproducible example here.

Best of luck with your modifications! If you have any more questions, feel free to ask.

Hi @glenn-jocher , thanks a lot for your response. Just an example like I wish to predict 2 different classes (one for each head) in an image, then what exactly should be added in the common.py file? Also in the yolov5 model, where exactly do we have to make changes in the loss functions to handle 2 heads?

[glenn-jocher]

Hi @jain-abhay,

Thank you for your follow-up question! Let's dive into the specifics of modifying common.py and the loss functions in yolo.py to handle two heads, each predicting different classes.

Modifying common.py

In common.py, you might not need extensive changes unless your heads require unique layers or blocks not already defined. However, if you need to add specific layers for each head, you can define them here. For example, if each head requires a custom convolutional layer, you can add it like this:

class CustomHead1(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(CustomHead1, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        return self.conv(x)

class CustomHead2(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(CustomHead2, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        return self.conv(x)

Modifying yolo.py

In yolo.py, you'll need to adjust the Model class to accommodate the two heads and modify the loss functions accordingly.

1. Model Definition:
   • Define the two heads in the Model class and adjust the forward pass to handle the outputs from both heads.

class Model(nn.Module):
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):
        super(Model, self).__init__()
        # Load model configuration
        self.yaml = self._load_yaml(cfg)
        # Define the backbone
        self.backbone = self._build_backbone(self.yaml['backbone'])
        # Define the two heads
        self.head1 = CustomHead1(in_channels=256, out_channels=nc1)  # Adjust channels as needed
        self.head2 = CustomHead2(in_channels=256, out_channels=nc2)  # Adjust channels as needed

    def forward(self, x):
        x = self.backbone(x)
        out1 = self.head1(x)
        out2 = self.head2(x)
        return out1, out2

2. Loss Computation:
   • Modify the loss computation to handle the outputs from both heads. You'll need to ensure that each head's loss is computed separately and then combined.

def compute_loss(pred1, pred2, targets, model):
    # Compute loss for head1
    loss1 = compute_loss_head1(pred1, targets, model)
    # Compute loss for head2
    loss2 = compute_loss_head2(pred2, targets, model)
    # Combine losses
    total_loss = loss1 + loss2
    return total_loss

def compute_loss_head1(pred, targets, model):
    # Define the loss computation for head1
    pass

def compute_loss_head2(pred, targets, model):
    # Define the loss computation for head2
    pass

Training Script

Ensure that your training script is updated to handle the outputs from both heads and compute the combined loss correctly.

for batch_i, (imgs, targets, paths, shapes) in enumerate(dataloader):
    imgs = imgs.to(device, non_blocking=True).float() / 255.0
    pred1, pred2 = model(imgs)
    loss = compute_loss(pred1, pred2, targets, model)
    loss.backward()
    optimizer.step()

By following these steps, you should be able to modify YOLOv5 to include a common backbone with two different heads, each predicting different classes. If you encounter any specific issues or need further assistance, please provide a minimum reproducible example of your code. This will help us better understand the problem and provide more accurate guidance. You can find more information on creating a minimum reproducible example here.

Best of luck with your modifications! If you have any more questions, feel free to ask. 😊

[jain-abhay]

Hi @jain-abhay,

Thank you for your follow-up question! Let's dive into the specifics of modifying common.py and the loss functions in yolo.py to handle two heads, each predicting different classes.

Modifying common.py

In common.py, you might not need extensive changes unless your heads require unique layers or blocks not already defined. However, if you need to add specific layers for each head, you can define them here. For example, if each head requires a custom convolutional layer, you can add it like this:

class CustomHead1(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(CustomHead1, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        return self.conv(x)

class CustomHead2(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(CustomHead2, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        return self.conv(x)

Modifying yolo.py

In yolo.py, you'll need to adjust the Model class to accommodate the two heads and modify the loss functions accordingly.

1. Model Definition:

   • Define the two heads in the Model class and adjust the forward pass to handle the outputs from both heads.

class Model(nn.Module):
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):
        super(Model, self).__init__()
        # Load model configuration
        self.yaml = self._load_yaml(cfg)
        # Define the backbone
        self.backbone = self._build_backbone(self.yaml['backbone'])
        # Define the two heads
        self.head1 = CustomHead1(in_channels=256, out_channels=nc1)  # Adjust channels as needed
        self.head2 = CustomHead2(in_channels=256, out_channels=nc2)  # Adjust channels as needed

    def forward(self, x):
        x = self.backbone(x)
        out1 = self.head1(x)
        out2 = self.head2(x)
        return out1, out2

2. Loss Computation:

   • Modify the loss computation to handle the outputs from both heads. You'll need to ensure that each head's loss is computed separately and then combined.

def compute_loss(pred1, pred2, targets, model):
    # Compute loss for head1
    loss1 = compute_loss_head1(pred1, targets, model)
    # Compute loss for head2
    loss2 = compute_loss_head2(pred2, targets, model)
    # Combine losses
    total_loss = loss1 + loss2
    return total_loss

def compute_loss_head1(pred, targets, model):
    # Define the loss computation for head1
    pass

def compute_loss_head2(pred, targets, model):
    # Define the loss computation for head2
    pass

Training Script

Ensure that your training script is updated to handle the outputs from both heads and compute the combined loss correctly.

for batch_i, (imgs, targets, paths, shapes) in enumerate(dataloader):
    imgs = imgs.to(device, non_blocking=True).float() / 255.0
    pred1, pred2 = model(imgs)
    loss = compute_loss(pred1, pred2, targets, model)
    loss.backward()
    optimizer.step()

By following these steps, you should be able to modify YOLOv5 to include a common backbone with two different heads, each predicting different classes. If you encounter any specific issues or need further assistance, please provide a minimum reproducible example of your code. This will help us better understand the problem and provide more accurate guidance. You can find more information on creating a minimum reproducible example here.

Best of luck with your modifications! If you have any more questions, feel free to ask. 😊

Thanks a lot @glenn-jocher for your suggestions. I will try this out and let you know!
Thanks and Regards

[glenn-jocher]

Hi @jain-abhay,

You're very welcome! I'm glad you found the suggestions helpful. 😊 Please do try them out and let us know how it goes. If you run into any issues or have further questions, feel free to reach out.

Remember, providing a minimum reproducible example can greatly assist us in diagnosing any problems you encounter. You can find more details on how to create one here.

Best of luck with your modifications, and we're here to help if you need any further assistance!

Thanks and

[jain-abhay]

Hi @jain-abhay,

You're very welcome! I'm glad you found the suggestions helpful. 😊 Please do try them out and let us know how it goes. If you run into any issues or have further questions, feel free to reach out.

Remember, providing a minimum reproducible example can greatly assist us in diagnosing any problems you encounter. You can find more details on how to create one here.

Best of luck with your modifications, and we're here to help if you need any further assistance!

Thanks and

Hi @glenn-jocher, just wanted to confirm a few things. The file common.py won't be altered here right since I am just defining 2 heads

Also in the file yolo.py, the following functions need to be changed right? :

1. class Detect(nn.Module):
2. class Model(nn.Module):
3. def forward(self, x, augment=False, profile=False):
4. def forward_once(self, x, profile=False):
5. def parse_model(d, ch):

Please can you kindly confirm this once? Thanks in anticipation

[glenn-jocher]

Hi @jain-abhay,

Thank you for your follow-up! Let's clarify your questions regarding the modifications needed for common.py and yolo.py.

common.py

You're correct that if you are only defining two heads for face and person detection, you might not need to alter common.py unless your heads require unique layers or blocks not already defined. If the existing layers suffice, you can proceed without changes to common.py.

yolo.py

Regarding yolo.py, you are on the right track. Here are the specific functions and classes you need to modify:

1. class Detect(nn.Module):

   • This class is responsible for the detection heads. You will need to modify it to handle two separate heads for face and person detection.

2. class Model(nn.Module):

   • This is the main model class. You will need to adjust it to define and initialize the two heads.

3. def forward(self, x, augment=False, profile=False):

   • Modify this function to handle the forward pass through both heads and return their outputs.

4. def forward_once(self, x, profile=False):

   • Similar to the forward function, ensure this handles the forward pass through both heads.

5. def parse_model(d, ch):

   • This function parses the model configuration. You will need to adjust it to correctly parse and set up the two heads.

Here's a brief outline of what changes might look like in yolo.py:

class Detect(nn.Module):
    def __init__(self, nc1, nc2, anchors, ch):  # number of classes for each head
        super(Detect, self).__init__()
        self.nc1 = nc1  # number of classes for head1
        self.nc2 = nc2  # number of classes for head2
        self.anchors = anchors
        self.head1 = nn.Conv2d(ch, nc1, 1)  # Define head1
        self.head2 = nn.Conv2d(ch, nc2, 1)  # Define head2

    def forward(self, x):
        return self.head1(x), self.head2(x)

class Model(nn.Module):
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc1=None, nc2=None, anchors=None):
        super(Model, self).__init__()
        self.yaml = self._load_yaml(cfg)
        self.backbone = self._build_backbone(self.yaml['backbone'])
        self.detect = Detect(nc1, nc2, anchors, ch)

    def forward(self, x, augment=False, profile=False):
        x = self.backbone(x)
        out1, out2 = self.detect(x)
        return out1, out2

    def forward_once(self, x, profile=False):
        x = self.backbone(x)
        out1, out2 = self.detect(x)
        return out1, out2

    def parse_model(self, d, ch):
        # Parse the model configuration and set up the two heads
        pass

Next Steps

Please ensure you have the latest versions of torch and yolov5 from the Ultralytics YOLOv5 GitHub repo. If you encounter any issues, providing a minimum reproducible example will greatly assist us in diagnosing the problem. You can find more details on how to create one here.

Feel free to reach out if you have any more questions or need further assistance. Best of luck with your modifications! 😊

Thanks and

[jain-abhay]

Hi @glenn-jocher , thanks for your inputs, will work on it. Also, I came across this issue and found your comment on it : #2001

**"Yes the https://github.com/ultralytics/yolov5/tree/classifier branch adds a classify.py file that does standalone classifier training. It does not attempt to merge tasks though.

There is a C5_divergent branch (https://github.com/ultralytics/yolov5/tree/C5_divergent) that examines some updated architectures that are similar to hydra nets. For example this model has one backbone and two heads:
https://github.com/ultralytics/yolov5/blob/C5_divergent/models/hub/yolov5l6d-640.yaml

Basically the yaml files are very flexible and can allow you to define a lot of interesting shapes such as the dual-head network above, which I was using to see if there was any benefit to having different heads compute different losses (i.e. a box regression head and a obj/cls head seperately). I didn't find any benefit from doing this strangely enough, so it's possible that in the case of the detection the loss components might complement each other. "**

Is this kind of similar to my task of one backbone 2 heads, also the links that you had mentioned aren't opening (https://github.com/ultralytics/yolov5/blob/C5_divergent/models/hub/yolov5l6d-640.yaml). Please, it would be really great if you could share updated links for these repositories. Thanks a lot for your help and guidance.

[glenn-jocher]

Hi @jain-abhay,

Thank you for your message and for sharing the context. I'm glad to hear that you're making progress with the modifications!

Regarding your question, yes, the concept of having one backbone with two heads is indeed similar to what you are aiming to achieve. The C5_divergent branch was an experimental branch exploring architectures with multiple heads, which aligns with your task.

I apologize for the broken links. It seems the C5_divergent branch and the associated files might have been moved or removed. However, you can still achieve your goal by following the guidance provided earlier and leveraging the flexibility of the YOLOv5 YAML configuration files.

To ensure we can assist you effectively, please verify the following:

1. Minimum Reproducible Example: If you encounter any issues, please provide a minimum reproducible code example. This will help us understand the problem better and provide a more accurate solution. You can find more details on how to create one here.
2. Latest Versions: Ensure you are using the most recent versions of torch and YOLOv5 from the Ultralytics YOLOv5 GitHub repo. If not, please upgrade your packages and try again.

If you need further assistance or have any specific questions, feel free to ask. We're here to help! 😊

[jain-abhay]

Hi @jain-abhay,

Thank you for your message and for sharing the context. I'm glad to hear that you're making progress with the modifications!

Regarding your question, yes, the concept of having one backbone with two heads is indeed similar to what you are aiming to achieve. The C5_divergent branch was an experimental branch exploring architectures with multiple heads, which aligns with your task.

I apologize for the broken links. It seems the C5_divergent branch and the associated files might have been moved or removed. However, you can still achieve your goal by following the guidance provided earlier and leveraging the flexibility of the YOLOv5 YAML configuration files.

To ensure we can assist you effectively, please verify the following:

1. Minimum Reproducible Example: If you encounter any issues, please provide a minimum reproducible code example. This will help us understand the problem better and provide a more accurate solution. You can find more details on how to create one here.
2. Latest Versions: Ensure you are using the most recent versions of torch and YOLOv5 from the Ultralytics YOLOv5 GitHub repo. If not, please upgrade your packages and try again.

If you need further assistance or have any specific questions, feel free to ask. We're here to help! 😊

Hi @glenn-jocher please can you explain how the yaml file works, specifically how did we get [[-1, 5]] in the third line of the head block and also how did we get the numbers [[16, 19, 22], in the last line of the head block?

parameters

nc: 2 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.185 # layer channel multiple

anchors

anchors:

• [4,5, 8,10, 13,16] # P3/8
• [23,29, 43,55, 73,105] # P4/16
• [146,217, 231,300, 335,433] # P5/32

YOLOv5 backbone

backbone:

[from, number, module, args]

[[-1, 1, StemBlock, [64, 3, 2]], # 0-P1/2
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 2-P3/8
[-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 4-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 6-P5/32
[-1, 1, SPP, [1024, [3,5,7], 'pad_value': 0]],
[-1, 3, C3, [1024, False]], # 8
]

YOLOv5 head

head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, ConvTranspose2d, [512, 2, 2]],
[[-1, 5], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 12

[-1, 1, Conv, [256, 1, 1]],
[-1, 1, ConvTranspose2d, [256, 2, 2]],
[[-1, 3], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 16 (P3/8-small)

[-1, 1, Conv, [256, 3, 2]],
[[-1, 13], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 19 (P4/16-medium)

[-1, 1, Conv, [512, 3, 2]],
[[-1, 9], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 22 (P5/32-large)

[[16, 19, 22], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]

[glenn-jocher]

Hi @jain-abhay,

Thank you for your detailed question! Let's break down the YOLOv5 YAML configuration file to understand how the head block works, specifically the [-1, 5] and [[16, 19, 22]] lines.

Understanding the YAML Configuration

The YOLOv5 YAML configuration file defines the architecture of the model, including the backbone and head. Each line in the backbone and head sections follows the format:

[from, number, module, args]

• from: Specifies the layers from which the input is taken. A negative number indicates layers relative to the current layer.
• number: Number of repetitions of the module.
• module: The type of module (e.g., Conv, C3, Concat).
• args: Arguments for the module.

Explanation of the head Block

Let's look at the specific lines you mentioned:

Line: [-1, 5]

[[-1, 5], 1, Concat, [1]]

• [-1, 5]: This indicates that the input to this layer comes from the current layer (-1) and the layer 5 steps back in the sequence. This is used to concatenate features from different layers.
• 1: Number of repetitions (in this case, just once).
• Concat: The module type, which concatenates the inputs along a specified dimension.
• [1]: The argument for Concat, indicating concatenation along the channel dimension.

This line essentially concatenates the output of the current layer with the output from 5 layers back.

Line: [[16, 19, 22]]

[[16, 19, 22], 1, Detect, [nc, anchors]]

• [16, 19, 22]: This specifies that the input to this layer comes from layers 16, 19, and 22. These layers correspond to different scales (P3, P4, P5) in the feature pyramid.
• 1: Number of repetitions (in this case, just once).
• Detect: The module type, which is responsible for making the final predictions.
• [nc, anchors]: Arguments for the Detect module, where nc is the number of classes, and anchors are the predefined anchor boxes.

This line sets up the detection head to take inputs from three different scales and make predictions.

Summary

• [-1, 5]: Concatenates the current layer's output with the output from 5 layers back.
• [[16, 19, 22]]: Sets up the detection head to take inputs from layers 16, 19, and 22, which correspond to different scales in the feature pyramid.

By understanding these configurations, you can see how the YOLOv5 model combines features from different layers to make robust predictions at multiple scales.

If you have any further questions or need additional clarification, feel free to ask. We're here to help! 😊

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