Incorporating Ghost Module and CBAM in YOLOv8 for Vehicle Detection & Comparison with Base Model

[UmerMustafa00]

Hello Ultralytics community,

I am currently working on vehicle detection using the YOLOv8 model and looking to implement some architectural modifications to improve performance. Specifically, I am looking to introduce the Ghost Module and CBAM (Convolutional Block Attention Module) in the neck or body layers of YOLOv8 to optimize the model for my vehicle detection task.

Here are a few key aspects of the project:

Goal: I want to compare the modified YOLOv8 (with Ghost and CBAM modules) to the base YOLOv8 model to evaluate any improvements in detection performance, particularly in terms of accuracy, inference speed, and model size.

Modifications:

Ghost Module: Aims to reduce the complexity of the convolution layers while maintaining the feature extraction power.
CBAM: Adding attention mechanisms to focus on significant features by using both spatial and channel-wise attention.
Comparison Metrics: I plan to compare the modified model with the base model based on mAP, accuracy, inference time, and model size.

Secondary Task: Additionally, I am working on a number plate detection task where I aim to increase the accuracy of my model. Any insights on how I can further enhance the model’s accuracy through layer modifications or additional tuning would be greatly appreciated.

My Queries:

What are the best practices or considerations when introducing Ghost and CBAM modules into the YOLOv8 architecture?
How should I modify the YOLOv8 architecture in the body or neck to incorporate these modules without causing significant trade-offs in speed or performance?
For number plate detection, are there specific architectural tweaks or additional modules I should consider to enhance accuracy and robustness?
I would appreciate any advice or examples from the community on how to approach these modifications and comparisons.

[UltralyticsAssistant]

👋 Hello @UmerMustafa00, thank you for sharing your intriguing project with the Ultralytics community 🚀! It sounds like you are undertaking an ambitious and exciting task by incorporating the Ghost Module and CBAM into YOLOv8 to enhance vehicle detection performance.

For new users and project undertakings like yours, we recommend browsing the Docs where you might find useful information about modifying architectures and optimizing models, including Python and CLI examples.

Regarding your architectural modifications, if this involves complex changes, we recommend providing a minimum reproducible example to help us replicate and analyze the potential impacts of your customizations 🐛.

To further improve on the number plate detection task and overall accuracy, you might also want to explore our Tips for Best Training Results, which can guide you through dataset preparation, hyperparameter tuning, and other optimization strategies.

Feel free to join our vibrant community discussions where many members are also exploring innovative ideas. For real-time support and interaction, consider joining the Discord 🎧. You can also gain deeper insights by checking out our Discourse forum or sharing your experiences on our Subreddit.

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Status

This is an automated response, and an Ultralytics engineer will review your queries and provide further guidance shortly. We appreciate your patience and engagement with the community! 😊

[glenn-jocher]

@UmerMustafa00 to incorporate Ghost and CBAM modules into YOLOv8, consider modifying the model's architecture by integrating these modules into the neck or body layers. Ensure you maintain a balance between complexity and performance to avoid significant trade-offs. For number plate detection, focus on fine-tuning hyperparameters and potentially using specialized datasets. For detailed guidance, refer to the YOLOv8 documentation and community forums.

[UmerMustafa00]

I could not find the artitecture change file where I changed these two functions and check the model accuracy again

[glenn-jocher]

@UmerMustafa00 please check the model's YAML configuration files for architecture changes, and ensure any modifications align with your intended updates.