Add a Regression Head to YOLOv8 via YAML or Manual Backbone Modification?

[MoAbbasid]

Hi Ultralytics team,

I’m extending YOLOv8 to predict clearance values (4 outputs) alongside detection.
My input is basically the same just plus 4 cm values added in the labeling file (is this the correct approach)

I’m considering two approaches:

• Modifying the YAML to add a regression head.

• Manually stripping the torch backbone and configuring the head, loss, and dataloader.

For the YAML approach, I’ve added this snippet:

# Regression Head
[-1, 1, Conv, [256, 3, 1]],  # Additional convolution for regression
[-1, 1, nn.AdaptiveAvgPool2d, [1]],  # Global average pooling
[-1, 1, nn.Flatten, []],  # Flatten
[-1, 1, nn.Linear, [256, 128]],  # Fc layer
[-1, 1, nn.ReLU, []],  # Activation
[-1, 1, nn.Linear, [128, nr]],  # Output: 4 clearance values

My questions are:

-What is the preferred way of making changes?

-Is modifying the YAML like I did enough? I need to define the loss somewhere right?

-If manual stripping is better, what changes are needed (the dataloader, loss function, head configuration) or more? am I missing something?

your guidance is appreciated!

[UltralyticsAssistant]

👋 Hello @MoAbbasid, thank you for your interest in Ultralytics 🚀! Extending YOLOv8 for regression tasks is an exciting challenge, and we’re happy to support you! Here’s how you can proceed:

If this is a 🐛 Bug Report, we’ll need a minimum reproducible example to help debug any issues during your implementation.

For a custom training ❓ Question, like yours, additional details about your setup will help guide our support. This can include configuration details, snippets of your dataset structure, and training logs. Also, consider reviewing best practices for designing custom tasks, as this can often make the difference in achieving optimal results.

Upgrade

Make sure your environment is up to date with the latest version of ultralytics and all dependencies. Running the latest version ensures you have the newest features and bug fixes available. Use the following command to upgrade:

pip install -U ultralytics

Environments

Testing your custom modification in verified development environments, such as notebooks with preconfigured GPUs or any up-to-date cloud platforms, can help ensure a smooth workflow. Proper dependencies and configurations for libraries like Python, PyTorch, and CUDA are crucial for avoiding runtime issues.

YAML and Manual Modifications

Your YAML snippet looks like a good start for adding a regression head. However, you’re correct that additional configuration will be needed for the loss function and potentially the dataloader if your input/output structure deviates from standard YOLO implementations.

If you choose the manual stripping route, you’ll need to reconfigure the following components:

• Backbone modifications: Ensure that the added layers integrate properly with the pre-existing structure.
• Loss function: Add and handle your regression-specific loss terms.
• Dataloader: Verify that your labeling structure is parsed correctly during training.

Both approaches have their merits. YAML modifications are simpler, but manual adjustments provide greater flexibility.

An Ultralytics engineer will review your discussion shortly and provide more tailored guidance based on your specific use case 😊. In the meantime, feel free to share more details about your dataset structure and the intended outputs!

[glenn-jocher]

@MoAbbasid thank you for your detailed query! Modifying the YAML is a good starting point for adding a regression head, but you are correct that defining a suitable loss function is also necessary to handle the additional regression outputs effectively. Here's a breakdown:

1. Preferred Approach: YAML modification is simpler and integrates well with the existing framework, but manual backbone manipulation offers more flexibility if your use case requires custom architecture changes.

2. YAML Modifications: While your YAML snippet looks good, you will also need to ensure:

   • The loss function accommodates both detection and regression tasks. You can define this in the custom training script.
   • The data loader correctly processes your additional labels (e.g., the 4 clearance values).

3. Manual Backbone Changes: If opting for this approach, you'll need to:

   • Adjust the model's forward pass to include the regression head.
   • Customize the loss function and dataloader to support regression outputs.
   • Ensure all changes align with the input-output expectations of the new model.

For additional guidance, refer to the Ultralytics YOLO documentation. Let us know if you need further clarification!