how to get mIoU and mPA in yolov5_seg? about yolov5

[github-actions]

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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

[glenn-jocher]

@Powerfulidot hello,

Thank you for reaching out and for your interest in using YOLOv5 for your segmentation tasks!

To calculate metrics like mean Intersection over Union (mIoU) and mean Pixel Accuracy (mPA) for YOLOv5 segmentation (yolov5_seg), you can follow these steps:

1. Update to the Latest Version: Ensure you are using the latest version of YOLOv5 from the Ultralytics repository and the latest version of torch. This ensures you have the latest features and bug fixes.

2. Custom Evaluation Script: YOLOv5 does not natively include mIoU and mPA metrics in its validation script. However, you can modify the validation script to include these metrics. Below is an example of how you might calculate these metrics:

import torch
import numpy as np
from utils.metrics import ConfusionMatrix

def calculate_mIoU_mPA(preds, targets, num_classes):
    cm = ConfusionMatrix(num_classes)
    cm.process_batch(preds, targets)
    iou = cm.iou()
    mIoU = iou.mean().item()
    mPA = (cm.tp / (cm.tp + cm.fn)).mean().item()
    return mIoU, mPA

# Example usage during validation
# preds and targets should be tensors of shape [batch_size, height, width]
preds = torch.argmax(predictions, dim=1)  # Assuming predictions are logits
mIoU, mPA = calculate_mIoU_mPA(preds, targets, num_classes=21)  # Adjust num_classes as needed
print(f'mIoU: {mIoU}, mPA: {mPA}')

3. Integrate with YOLOv5 Validation: You can integrate the above function into the YOLOv5 validation loop. Modify the val.py script to include calls to calculate_mIoU_mPA and print or log the results.

4. Comparative Experiments: Once you have integrated these metrics, you can run your validation and compare the results with other semantic segmentation algorithms like Unet and PSPNet.

If you encounter any issues or need further assistance, please provide a minimum reproducible code example so we can better understand and address your specific situation. You can find guidance on creating a minimum reproducible example here.

We hope this helps! If you have any more questions, feel free to ask. 😊

[Powerfulidot]

@Powerfulidot hello,

Thank you for reaching out and for your interest in using YOLOv5 for your segmentation tasks!

To calculate metrics like mean Intersection over Union (mIoU) and mean Pixel Accuracy (mPA) for YOLOv5 segmentation (yolov5_seg), you can follow these steps:

1. **Update to the Latest Version**: Ensure you are using the latest version of YOLOv5 from the [Ultralytics repository](https://github.com/ultralytics/yolov5) and the latest version of `torch`. This ensures you have the latest features and bug fixes.

2. **Custom Evaluation Script**: YOLOv5 does not natively include mIoU and mPA metrics in its validation script. However, you can modify the validation script to include these metrics. Below is an example of how you might calculate these metrics:

import torch
import numpy as np
from utils.metrics import ConfusionMatrix

def calculate_mIoU_mPA(preds, targets, num_classes):
    cm = ConfusionMatrix(num_classes)
    cm.process_batch(preds, targets)
    iou = cm.iou()
    mIoU = iou.mean().item()
    mPA = (cm.tp / (cm.tp + cm.fn)).mean().item()
    return mIoU, mPA

# Example usage during validation
# preds and targets should be tensors of shape [batch_size, height, width]
preds = torch.argmax(predictions, dim=1)  # Assuming predictions are logits
mIoU, mPA = calculate_mIoU_mPA(preds, targets, num_classes=21)  # Adjust num_classes as needed
print(f'mIoU: {mIoU}, mPA: {mPA}')

3. **Integrate with YOLOv5 Validation**: You can integrate the above function into the YOLOv5 validation loop. Modify the `val.py` script to include calls to `calculate_mIoU_mPA` and print or log the results.

4. **Comparative Experiments**: Once you have integrated these metrics, you can run your validation and compare the results with other semantic segmentation algorithms like Unet and PSPNet.

If you encounter any issues or need further assistance, please provide a minimum reproducible code example so we can better understand and address your specific situation. You can find guidance on creating a minimum reproducible example here.

We hope this helps! If you have any more questions, feel free to ask. 😊

thank you so much for such quick reply but i ve achieved it just now. thank you anyway!

[glenn-jocher]

Hello @Powerfulidot,

Thank you for your kind words! I'm glad to hear that you've successfully achieved your goal. 🎉

If you have any further questions or need assistance with anything else related to YOLOv5, please don't hesitate to reach out. The YOLO community and the Ultralytics team are always here to help.

Happy experimenting and best of luck with your comparative studies!

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

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