How to modify the network structure of the YOLOv5 classification model

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Hello, I tried to change the example of YOLOv5 model=YOLO ('yolov5s-cls. yaml '). load ('yolov5s-cls. pt') to the corresponding YOLOv5-cls yaml and pt files, but encountered an error AttributeError: Can't get attribute 'Classification Model' on<module 'models. yolo' from'D: \ \ anaco \ \ envs \ \ yolo8 \ \ Lib \ \ site packages \ \ ultralytics \ \ models \ \ yolo \ \ init. py '>. How can I solve this problem? I tried importing only the YAML file and it ran correctly. But this cannot read the weight of the PT file. Or is there any way to operate in yolov5 master to change the yolov5 classification network structure. problem

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Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

@lielumao hello,

Thank you for reaching out and providing detailed information about the issue you're encountering. It looks like you're trying to modify the YOLOv5 classification model and are running into an AttributeError.

To address this, please follow these steps:

Ensure Latest Version: First, make sure you are using the latest version of YOLOv5. You can update it by running:
```
pip install --upgrade ultralytics
```

Correct Model Loading: When loading a classification model, ensure you are using the correct syntax. For example:

from ultralytics import YOLO

model = YOLO('yolov5s-cls.yaml')  # Load model architecture
model.load_weights('yolov5s-cls.pt')  # Load model weights

Modify Network Structure: If you need to modify the network structure, you can edit the .yaml file directly. For example, you can change the number of layers, filters, etc., in the yolov5s-cls.yaml file.
Custom Model: If you are creating a custom model, ensure that the custom layers or modifications are correctly defined in the .yaml file and that the corresponding weights file is compatible.

Here's an example of how you might modify the yolov5s-cls.yaml file:

# Parameters
nc: 1000  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple

# YOLOv5 backbone
backbone:
  [[-1, 1, Conv, [64, 3, 1]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, Bottleneck, [128]],  # 2
   ...
  ]

# YOLOv5 head
head:
  [[-1, 1, Conv, [256, 3, 2]],  # 13-P5/32
   [-1, 1, Conv, [512, 3, 2]],  # 14-P6/64
   ...
  ]

Debugging: If you still encounter issues, try running a minimal example to isolate the problem. For instance, load the model without modifications to ensure the base setup works:
```
model = YOLO('yolov5s-cls.yaml')
model.load_weights('yolov5s-cls.pt')
```

If the problem persists, please provide additional details or error logs, and we can further investigate. The YOLO community and the Ultralytics team are here to help! 😊

Hello, I currently have a solution that involves changing the network structure in the object detection model of YOLOv5, training a PT model, and then applying this model to train.by in the classification network. This will automatically truncate the loaded object detection network and load a classification header, thereby changing the network structure of the YOLOv5 classification model. Also, the solution you provided seems to be ineffective. Thank you for your help.

Hello @lielumao,

Thank you for sharing your approach and for your patience. It sounds like you have a creative solution for modifying the network structure of the YOLOv5 classification model by leveraging the object detection model. Here are a few additional suggestions and clarifications that might help streamline your process:

Ensure Compatibility: When transitioning from an object detection model to a classification model, ensure that the layers and weights are compatible. This involves correctly mapping the layers from the detection model to the classification model.

Custom Head Addition: If you are adding a custom classification head, you can manually define this in your model script. Here's an example of how you might add a custom classification head to a pre-trained YOLOv5 model:

import torch
from ultralytics import YOLO

# Load the object detection model
model = YOLO('yolov5s.pt')

# Modify the model to add a classification head
model.model[-1] = torch.nn.Linear(model.model[-1].in_features, num_classes)  # Replace the last layer with a classification layer

# Save the modified model
torch.save(model.state_dict(), 'yolov5s_cls_custom.pt')

Training the Modified Model: Once you have modified the model, you can proceed with training it on your classification dataset:

# Load the modified model
model = YOLO('yolov5s_cls_custom.yaml')
model.load_weights('yolov5s_cls_custom.pt')

# Train the model
model.train(data='path/to/your/classification/dataset', epochs=50)

Verify Latest Versions: If you encounter any issues, please ensure you are using the latest versions of the YOLOv5 package and dependencies. This can help avoid any bugs that may have been fixed in recent updates.
Community Support: For further assistance, consider engaging with the YOLOv5 community on GitHub Discussions or other forums. The community is a great resource for troubleshooting and sharing innovative solutions.

I hope these suggestions help you refine your approach. If you have any further questions or run into specific issues, feel free to share more details, and we'll be happy to assist you further. Thank you for your contributions and for being a part of the YOLO community! 😊

ultralytics / yolov5