Why are the max_channels in the backbone of the classification model and the detection model different?

[LittleRain626]

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Hi, thank you very much for your work!

Recently I have been studying semi-supervised object detection based on yolov8. In order to verify the effectiveness of the semi-supervised algorithm, the backbone of the detection model needs to use the backbone of the classification model pre-trained on ImageNet, but the configuration file you provided (L model) The max_channels of the classification model backbone is 1024, while the maximum number of channels of the detection model backbone is 512. Why don't the two need to be consistent?

Looking forward to your reply!

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[glenn-jocher]

Hello! Thank you for reaching out and for your kind words! 😊

The difference in max_channels between the classification and detection backbones in YOLOv8 often stems from their specific task requirements and model architectural optimization. Classification models may use larger channel numbers to capture richer global information across the entire image, which benefits the task of image-level prediction. On the other hand, detection models balance between accuracy and inference speed, typically requiring broader, context-aware features at multiple scales, hence often optimized with fewer channels to maintain a faster computation speed and lower memory usage during the bounding box prediction process.

When adapting a classification backbone for detection, the key is to ensure it efficiently captures both spatial hierarchies and high-level semantic features, which occasionally requires differing architecture settings, such as max_channels.

If you're experimenting with semi-supervised learning, it might be necessary to adjust the architectures (and their configurations) to find the optimal balance for both detection performance and efficient learning.

Hope this helps! Let me know if you have any more questions or need further assistance with your experiments. Happy coding! 🚀

[LittleRain626]

Hello! Thank you for reaching out and for your kind words! 😊

The difference in max_channels between the classification and detection backbones in YOLOv8 often stems from their specific task requirements and model architectural optimization. Classification models may use larger channel numbers to capture richer global information across the entire image, which benefits the task of image-level prediction. On the other hand, detection models balance between accuracy and inference speed, typically requiring broader, context-aware features at multiple scales, hence often optimized with fewer channels to maintain a faster computation speed and lower memory usage during the bounding box prediction process.

When adapting a classification backbone for detection, the key is to ensure it efficiently captures both spatial hierarchies and high-level semantic features, which occasionally requires differing architecture settings, such as max_channels.

If you're experimenting with semi-supervised learning, it might be necessary to adjust the architectures (and their configurations) to find the optimal balance for both detection performance and efficient learning.

Hope this helps! Let me know if you have any more questions or need further assistance with your experiments. Happy coding! 🚀

Thank you for your prompt and clear reply!

I have another question that I would like your help with. Currently, when I perform semi-supervised tasks, I use the backbone weights of the officially provided classification model to initialize the backbone of the detection model. In order to solve the problem of the inconsistency between the max_channels of the detection model and the classification model, I changed the max_channels of the detection model to 1024. The detection model constructed in this way can indeed complete semi-supervised tasks and achieve good results in accuracy indicators. However, due to the increase in the max_channels, the number of parameters and FLOPs of the model also increase. Compared with It has no advantages over other semi-supervised algorithms.

May I ask if yolov8 official detection model uses the backbone trained on ImageNet to initialize it when training? If so, could you please provide the classification model with a max_channels of 512? If not, do I need to fine-tune the structure of the classification model to ensure that yolov8 has the advantages of detectors used by other semi-supervised algorithms in terms of parameter size and floating point operations?

Thanks and looking forward to your reply!

[glenn-jocher]

Hello again!

It's great to hear about your progress and the results you are achieving with the YOLOv8 model! Regarding your questions:

1. Using ImageNet-trained backbones in YOLOv8 Models: The official YOLOv8 detection models do not typically start from an ImageNet pre-trained backbone. They are generally trained from scratch or using weights from previous YOLO models.

2. Providing a Classification Model with max_channels of 512: Since the YOLOv8 does not generally employ ImageNet-trained backbones directly for detection tasks, we do not provide specific classification models tailored to detection max_channels configurations like 512. However, you are on the right track by fine-tuning the structure to adapt it to your semi-supervised setting.

3. Handling Increased FLOPs and Parameters: If increasing max_channels results in higher computational costs without proportional gains, indeed, optimizing the model structure is advisable. Consider experimenting with varying depths, widths, and potentially using techniques like pruning or knowledge distillation to craft a more efficient model that retains high accuracy yet reduces computation overhead.

Adjusting these aspects should help maintain the performance benefits while managing the computational footprint, essential for keeping an edge over other semi-supervised approaches.

Wish you all the best in your experiments! Let me know if there's anything else you need. 👍

[LittleRain626]

It's great to hear about your progress and the results you are achieving with the YOLOv8 model! Regarding your questions:

1. Using ImageNet-trained backbones in YOLOv8 Models: The official YOLOv8 detection models do not typically start from an ImageNet pre-trained backbone. They are generally trained from scratch or using weights from previous YOLO models.
2. Providing a Classification Model with max_channels of 512: Since the YOLOv8 does not generally employ ImageNet-trained backbones directly for detection tasks, we do not provide specific classification models tailored to detection max_channels configurations like 512. However, you are on the right track by fine-tuning the structure to adapt it to your semi-supervised setting.
3. Handling Increased FLOPs and Parameters: If increasing max_channels results in higher computational costs without proportional gains, indeed, optimizing the model structure is advisable. Consider experimenting with varying depths, widths, and potentially using techniques like pruning or knowledge distillation to craft a more efficient model that retains high accuracy yet reduces computation overhead.

Adjusting these aspects should help maintain the performance benefits while managing the computational footprint, essential for keeping an edge over other semi-supervised approaches.

Wish you all the best in your experiments! Let me know if there's anything else you need. 👍

Much obliged for your guidance, and your reply has been a great help！Have a nice day! ^_^

[glenn-jocher]

You're very welcome! I'm glad the information was helpful. If you have any more questions in the future or need further assistance, feel free to reach out. Have a fantastic day and happy coding! 😊