How many layers is the backbone in the segmentation model of YoloV8?

nachoogriis commented 1 month ago

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Question

I am working in a person segmentation problem with a personal dataset. To obtain good results I want to do some fine-tuning. As the amount of data I have is not extremely high, I would want to perform fine-tuning while freezing some layers.

For freezing, I was considering to freeze the backbone, while fine-tuning the heads. My questions are:

Which has to be the value of the freeze parameter in training in order to freeze the backbone of the model? The freeze parameter freezes the first $N$ layers.
Is there any better place to place the freezing. This is, which might be a good value for the freeze parameter?

Additional

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glenn-jocher commented 1 month ago

@nachoogriis hello,

Thank you for your question! The backbone in the YOLOv8 segmentation model consists of 12 layers. To freeze the backbone during fine-tuning, you can set the freeze parameter to 12. This will freeze the first 12 layers, allowing you to fine-tune the head layers.

Here's an example of how to set the freeze parameter:

yolo segment train data=your_dataset.yaml model=yolov8n-seg.pt epochs=100 freeze=12

Freezing the backbone while fine-tuning the head layers is a common approach, especially when working with a limited amount of data. It helps in retaining the learned features from the backbone while adapting the head layers to your specific dataset.

If you have any further questions or need additional assistance, feel free to ask. Happy training! 🚀

nachoogriis commented 1 month ago

Thank you so much. Moreover, I have been trying to train the YoloV8-seg model (the same than in the previous question) on my new dataset. To work with different experiments, I wanted to try to execute, at first, the model with random weight inizialization. I have done the following code.

model_file_path = "/kaggle/input/segmentation-models/yolov8m-seg.pt"
config_file_path = "/kaggle/input/config-data/config.yaml"
hyps_file_path = "/kaggle/input/config-data/hyps.yaml"

model = YOLO(model_file_path)  

model.train(data = config_file_path, epochs = 150, patience = 50, imgsz = 640, pretrained = False, name = "YOLO random weights base hyps")

However, this gives exactly the same results than executing the model with the pretrained weights. The code done to execute pretrained weights is the following.

model_file_path = "/kaggle/input/segmentation-models/yolov8m-seg.pt"
config_file_path = "/kaggle/input/config-data/config.yaml"
hyps_file_path = "/kaggle/input/config-data/hyps.yaml"

model = YOLO(model_file_path)  

model.train(data = config_file_path, epochs = 150, patience = 50, imgsz = 640, name = "YOLO random weights base hyps")

What can be happening? Are both the pretrained version or are both the random version?

glenn-jocher commented 1 month ago

Hello @nachoogriis,

Thank you for reaching out with your query. It seems there might be a misunderstanding in how the model initialization is handled in your code.

When you specify pretrained=False, it should indeed initialize the model with random weights rather than loading pretrained weights. If you're observing that the results are the same in both cases, it's possible that the model is not correctly recognizing the pretrained flag due to how the model is instantiated.

To ensure that the model starts with random weights, you might want to explicitly load a model configuration file (YAML) rather than a pre-trained model file (PT). Here’s how you can modify your code:

model = YOLO('path/to/model.yaml')  # Ensure this is a YAML file, not a PT file

model.train(data=config_file_path, epochs=150, patience=50, imgsz=640, pretrained=False, name="YOLO random weights base hyps")

This approach ensures that the model is initialized as defined in the YAML configuration, without loading any pretrained weights. Please try this and let me know if it resolves the issue!

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