changing the backbone network to shufflenetv2

[nithin200417]

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hi i am a student interested in computer vision and yolo frameworks currently i am working on a project to classify the plant diseases using the yolo framework i have some questions regarding the project and thank in advance : 1) I want to change the yolo framework in such a way that is uses PCFAN for the feature extraction and generation of feature maps from the images can you please help me know whether it is possible and if possible how? 2) I also want to know about changing the classification process used in the yolov8 to shufflenetv2 pretrained model will it be enough if i create another file called as yolov8-cls-shufflenetv2.yaml and code it as

Ultralytics YOLO 🚀, AGPL-3.0 license

YOLOv8-cls image classification model with ShuffleNetV2. For Usage examples see https://docs.ultralytics.com/tasks/classify

Parameters

nc: 1000 # number of classes scales: # model compound scaling constants

[depth, width, max_channels]

n: [0.33, 0.25, 1024] s: [0.33, 0.50, 1024] m: [0.67, 0.75, 1024] l: [1.00, 1.00, 1024] x: [1.00, 1.25, 1024]

YOLOv8.0n backbone with ShuffleNetV2

backbone:

[from, repeats, module, args]

• [-1, 1, ShuffleNetV2Layer, [24, 1, True, 1]] # 0-P1/2
• [-1, 1, ShuffleNetV2Layer, [48, 1, False, 3]] # 1-P2/4
• [-1, 1, ShuffleNetV2Layer, [96, 2, False, 4]] # 2-P3/8
• [-1, 1, ShuffleNetV2Layer, [192, 2, False, 6]] # 3-P4/16
• [-1, 1, ShuffleNetV2Layer, [1024, 2, False, 3]] # 4-P5/32

YOLOv8.0n head

head:

• [-1, 1, Classify, [nc]] # Classify will this do or there are any other steps i should follow. i would really appreciate your answer on these questions, and once again thanks in advance.

Additional

No response

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[glenn-jocher]

Hello! It's great to see your enthusiasm for computer vision and YOLO frameworks. To address your questions:

1) Integrating PCFAN for feature extraction: Customizing YOLO to use a different feature extraction technique, like PCFAN, is possible but requires substantial modifications to the model architecture and possibly training procedures. You'd have to replace the existing backbone in the YOLO architecture with PCFAN, ensuring the dimensions and scalability suit YOLO's requirements. This involves editing the YAML architecture file and might also require custom layer implementations if PCFAN layers differ significantly from those used in YOLO.

2) Using ShuffleNetV2 for classification within YOLO: Your approach to integrating ShuffleNetV2 as a classification backbone for YOLOv8 by defining a YAML configuration file seems correct! Your YAML snippet correctly outlines the structure for employing ShuffleNetV2 within a YOLOv8 framework. However, ensure that ShuffleNetV2Layer corresponds to the implementation details of the ShuffleNetV2 layers you intend to use and that all dependencies and layer configurations are correctly defined. It's also necessary to adapt or rewrite parts of the model code to correctly integrate the new backbone during the model initialization phase, especially if ShuffleNetV2Layer is not already an existing layer type in the codebase.

Remember, while configuring the YAML files is a good start, the actual implementation details (e.g., defining the ShuffleNetV2Layer if it's custom) are crucial. Make sure any new custom layers or modifications are supported by the rest of the YOLO framework code.

Overall, both modifications are technically possible but require a good understanding of the YOLO codebase and Deep Learning model building. Your journey into adapting YOLOv8 for your project sounds exciting, and I hope these directions help you progress. Good luck! 🚀

[nithin200417]

Hello! It's great to see your enthusiasm for computer vision and YOLO frameworks. To address your questions:

1. Integrating PCFAN for feature extraction: Customizing YOLO to use a different feature extraction technique, like PCFAN, is possible but requires substantial modifications to the model architecture and possibly training procedures. You'd have to replace the existing backbone in the YOLO architecture with PCFAN, ensuring the dimensions and scalability suit YOLO's requirements. This involves editing the YAML architecture file and might also require custom layer implementations if PCFAN layers differ significantly from those used in YOLO.
2. Using ShuffleNetV2 for classification within YOLO: Your approach to integrating ShuffleNetV2 as a classification backbone for YOLOv8 by defining a YAML configuration file seems correct! Your YAML snippet correctly outlines the structure for employing ShuffleNetV2 within a YOLOv8 framework. However, ensure that ShuffleNetV2Layer corresponds to the implementation details of the ShuffleNetV2 layers you intend to use and that all dependencies and layer configurations are correctly defined. It's also necessary to adapt or rewrite parts of the model code to correctly integrate the new backbone during the model initialization phase, especially if ShuffleNetV2Layer is not already an existing layer type in the codebase.

Remember, while configuring the YAML files is a good start, the actual implementation details (e.g., defining the ShuffleNetV2Layer if it's custom) are crucial. Make sure any new custom layers or modifications are supported by the rest of the YOLO framework code.

Overall, both modifications are technically possible but require a good understanding of the YOLO codebase and Deep Learning model building. Your journey into adapting YOLOv8 for your project sounds exciting, and I hope these directions help you progress. Good luck! 🚀

thanks for the reply can you please guide me how to implement the shufflenetv2layer where to add the code for the shufflenetv2layers in the code base for my new backbone to work and really thanks for the reply .

[glenn-jocher]

Hi there! 😊 It's great to see you're ready to dive into customizing YOLOv8 with ShuffleNetV2. To implement ShuffleNetV2Layer, you'll likely need to:

1. Implement the Layer: Create a Python class for ShuffleNetV2Layer. If you're incorporating it into the Ultralytics repository, you might want to place this new class in a suitable file where other model layers are defined, like within the models directory. Here's a very basic example to get you started:

import torch
import torch.nn as nn

class ShuffleNetV2Layer(nn.Module):
    def __init__(self, input_channels, output_channels):
        super(ShuffleNetV2Layer, self).__init__()
        # Define your ShuffleNetV2 layer here
        self.conv = nn.Conv2d(input_channels, output_channels, kernel_size=1, stride=1)
        # Add more components as per ShuffleNetV2 requirements

    def forward(self, x):
        # Define the forward pass
        x = self.conv(x)
        return x

2. Integrate the Layer: After defining your layer, you'll need to modify the model's architecture file (potentially a YAML file for a YOLOv8 model) to use your new ShuffleNetV2Layer. You already outlined how you'd do this in your proposed YAML. Make sure your YAML references match the implementations and expected parameters.

3. Adjust the Model Initialization: Depending on how the Ultralytics YOLO codebase is structured, integrating your new backbone might require adjustments in the model initialization code. Look for where the model layers are instantiated based on the architecture file (often this happens in something like a build_model function) and ensure your new ShuffleNetV2Layer is properly handled.

Remember, integrating a new backbone into YOLOv8 isn't just about swapping out layers; it's about ensuring the entire model trains correctly with the new architecture. Testing and possibly adapting the training code to support the nuances of ShuffleNetV2 (such as different feature map sizes or aspect ratios) will be essential steps.

Thanks for your question, and don't hesitate to reach out again if you're stuck or need more details. Happy coding! 🚀

[nithin200417]

Hello, I would also like to try changing the backbone of yolov8 to detect leaf disease to a shufflenetv2 architecture, would you be able to guide me? im not sure where i can find this model.yaml or the files you mentioned as it is not in the directory where i did the pip install ultralytics. Im pretty new to this stuff. Would you be able to guide me somehow? Anything would do, thanks

[glenn-jocher]

Hello! Great to hear you're experimenting with YOLOv8 and ShuffleNetV2 for leaf disease detection! 🌿

To integrate ShuffleNetV2 into YOLOv8, you typically need to define a custom model YAML file and implement the ShuffleNetV2 layers in code. Since the YAML files or specific implementation details aren't directly accessible via the pip-installed package, you'll work with them in the source code. Here’s a brief step-by-step guidance:

1. Clone the Ultralytics GitHub repository to get access to the full codebase:

git clone https://github.com/ultralytics/ultralytics.git
cd ultralytics

2. Locate or create your model YAML file: In the repository, navigate to the /models directory. If you’re creating a new model configuration with ShuffleNetV2, you might need to create a new .yaml file here, say yolov8-shufflenetv2.yaml.

3. Define your ShuffleNetV2 Layer: In the models directory or a suitable place, implement the ShuffleNetV2 block as a PyTorch module. You might need to write custom code similar to the example in the previous reply.

4. Adjust the Model Architecture YAML: Reference your ShuffleNetV2 layers in the new .yaml configuration you're creating.

Once these steps are done, adjust your training scripts to use this new YAML.

If you're new to this, take it one step at a time, and don't hesitate to dive into the documentation or reach out for more specific queries. Good luck with your project! 👍

[Vibion]

Hello! Great to hear you're experimenting with YOLOv8 and ShuffleNetV2 for leaf disease detection! 🌿

To integrate ShuffleNetV2 into YOLOv8, you typically need to define a custom model YAML file and implement the ShuffleNetV2 layers in code. Since the YAML files or specific implementation details aren't directly accessible via the pip-installed package, you'll work with them in the source code. Here’s a brief step-by-step guidance:

1. Clone the Ultralytics GitHub repository to get access to the full codebase:

git clone https://github.com/ultralytics/ultralytics.git
cd ultralytics

2. Locate or create your model YAML file: In the repository, navigate to the /models directory. If you’re creating a new model configuration with ShuffleNetV2, you might need to create a new .yaml file here, say yolov8-shufflenetv2.yaml.
3. Define your ShuffleNetV2 Layer: In the models directory or a suitable place, implement the ShuffleNetV2 block as a PyTorch module. You might need to write custom code similar to the example in the previous reply.
4. Adjust the Model Architecture YAML: Reference your ShuffleNetV2 layers in the new .yaml configuration you're creating.

Once these steps are done, adjust your training scripts to use this new YAML.

If you're new to this, take it one step at a time, and don't hesitate to dive into the documentation or reach out for more specific queries. Good luck with your project! 👍

Hello :blush:~ if I've added a new network structure and I want to run it on another computer, what should I do? Since I modified the contents of the Ultralytics package, do I need to copy the entire working directory, or should I clone the repository from https://github.com/ultralytics/ultralytics.git on the other computer and then copy the modified files over?

[glenn-jocher]

Hello! 😊 You're on the right track! Since you've customized the Ultralytics package with a new network structure, the easiest approach is to copy the entire modified directory to the other computer. This ensures that all your changes are preserved exactly as you've made them.

Alternatively, if you're collaborating or want a cleaner setup, you could:

1. Clone the Ultralytics repository on the other computer.
2. Copy only your modified files or new additions (e.g., your custom .yaml and any new layer implementation files) into the respective directories of the freshly cloned repo.

Both methods are valid, but copying the whole directory might be simpler and ensures you don't miss any modifications. Good luck with your continued work on YOLOv8 and ShuffleNetV2! 🍀

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