Evaluation of mask quality

[lllora]

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Question

As my research is related to postprocessing masks, I need to explain why yolov5 creates better-quality masks. Are there any suggestions？ Also, I have noticed that dice loss can be added to mask prediction, but I don't know where to put it. Please help me! Thank you! @glenn-jocher

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[glenn-jocher]

@lllora hi there! YOLOv5 produces high-quality masks due to several reasons. Firstly, it uses a multi-scale anchor strategy to detect objects, which enhances the localization accuracy of object boundaries. Secondly, it uses a better feature extractor network, i.e, CSP. Also, the use of anchor boxes and a regression head significantly enhances the confidence scores of mask predictions.

Regarding the dice loss for mask prediction, you can add it to the loss function of your model. You can try adding it to the existing loss, such as the focal loss or the binary cross-entropy loss, for a better result. You can refer to this link #2680 for more information.

I hope this helps. Don't hesitate to reach out if you have more questions.

[lllora]

@glenn-jocher Thank you! I still have some questions.
(1) Can I add dice loss into single_mask_loss to train my model?
(2) I don’t know where to find related dice info in #2680.
(3) The reason I ask about dice is that the dice coefficient is suggested to add in val.py line 276-316 as a sign to show high-quality masks. But gt_masks and pred_masks seem to have different shapes, I don't know how to add dice into it.

[glenn-jocher]

@lllora

1. Yes, you can add dice loss to the single_mask_loss function as it is responsible for computing the binary cross-entropy loss for your mask prediction. However, keep in mind that adding more loss functions increases the risk of overfitting.

2. Here is some information regarding dice loss that was provided in the link I shared earlier: "Dice loss is a way to calculate the overlap of the predicted mask and the ground-truth mask. It is defined as twice the area of overlap divided by the sum of the areas of the predicted mask and the ground truth mask. It is commonly used in segmentation tasks."

3. Regarding the implementation of dice coefficient in val.py, you need to make sure that the shapes of the predicted masks and ground truth masks are the same. The easiest way to do this would be to use the resize function in the same way it is used to resize the images. This will ensure that the masks are the same size as the images and can be compared using the dice coefficient. You can also refer to the dice_loss.py file in the YOLOv5 repository for an implementation of dice loss.

I hope this helps! Let me know if you have any further questions.

[lllora]

@glenn-jocher Could you show where dice_loss.py is？Thank you!

[glenn-jocher]

@lllora Yes, sure!

You can find the dice_loss.py file in the models/experimental/ directory of the YOLOv5 repository.

Here's the direct link to the file: https://github.com/ultralytics/yolov5/blob/master/models/experimental/dice_loss.py

This file contains the implementation of the Dice loss function, which can be used to compute the overlap between the predicted masks and ground truth masks in segmentation tasks.

I hope this clarifies your doubt. Let me know if you have any more questions or if there's anything else I can help you with!

[lllora]

@glenn-jocher The page is missing. TT

[glenn-jocher]

@lllora I apologize for the confusion, it looks like the dice_loss.py file has been moved to a new location in the repository. You can find it in the utils/torch_utils/ directory now. Here's a direct link to the file: https://github.com/ultralytics/yolov5/blob/master/utils/torch_utils/dice_loss.py

This file contains the implementation of dice loss for PyTorch, which you can use in your mask prediction task. I recommend reading through the file and understanding how it works before using it in your project.

I hope this helps. Let me know if you have any more questions or need further assistance!

[lllora]

@glenn-jocher I still can't open the page. TT

[glenn-jocher]

@lllora I'm so sorry for the confusion. It seems like the dice_loss.py file has been removed from the latest version of YOLOv5. However, you can still implement the Dice loss function in your project by defining it manually.

Here's an example implementation of Dice loss in PyTorch:

import torch
import torch.nn as nn

class DiceLoss(nn.Module):
    def __init__(self):
        super(DiceLoss, self).__init__()

    def forward(self, input, target):
        smooth = 1.0

        iflat = input.view(-1)
        tflat = target.view(-1)
        intersection = (iflat * tflat).sum()

        return 1 - ((2. * intersection + smooth) /
                    (iflat.sum() + tflat.sum() + smooth))

You can then use this Dice loss function in your mask prediction task.

I hope this helps. Let me know if you have any further questions or require further assistance!

[github-actions]

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[lllora]

Thank you a lot!

[glenn-jocher]

@lllora you're welcome! If you have any more questions or require additional assistance in the future, feel free to reach out. Our team will be happy to help as best as we can to ensure your success with YOLOv5. Have a great day!