what is the width_multiple?

sihogu commented 2 weeks ago

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Hi, always grateful to use your YOLO. I have a question.

I modified 'yolov5.yaml' by changing "s = [0.50, 0.50, 1024]" to "s =[0.44, 0.33, 1024]".

I understand that the depth_multiple controls the number of Darknetbottleneck blocks.

However, I don’t fully understand what width_multiple does. I know it generally affects the convolution channels, but I want to know exactly what it controls and how. Could you explain this in detail? Thanks.

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UltralyticsAssistant commented 2 weeks ago

👋 Hello @sihogu, thank you for your interest in YOLOv5 🚀!

If this is a custom training ❓ Question, please provide as much information as possible to help us assist you better. While I'm providing an automated response right now, please know that an Ultralytics engineer will be with you soon to provide more detailed assistance.

Meanwhile, if you're asking about the width_multiple, it's great to hear that you're diving deep into YOLOv5's architecture modifications! The width_multiple parameter is indeed related to the convolutional channels. It helps scale the number of channels across various layers of the network, affecting model capacity and computational demand. This is especially useful for tailoring models to specific hardware or runtime requirements.

For any bug reports or more detailed debugging, please ensure to provide a minimum reproducible example to help us understand the issue better and assist you efficiently.

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Ensure you have Python >=3.8.0 and all dependencies installed as specified in the requirements.txt file. Use the following steps to set up:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

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YOLOv5 is pretty flexible and can be run in various environments, including online notebooks with free GPUs, Google Cloud, Amazon AWS, or through Docker images.

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We appreciate your patience and understanding as we work to resolve your query! 😊

pderrenger commented 2 weeks ago

@sihogu the width_multiple in YOLOv5 adjusts the number of convolutional channels in the model layers, effectively scaling the model's width. This parameter allows you to control the trade-off between model size and performance, impacting the computational cost and accuracy. For more detailed insights, you might want to explore the model configuration files or the Ultralytics documentation.

ultralytics / yolov5