How does the confusion matrix of the object detection module works? about ultralytics

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[WhCanI]

I just turn iou_thresh from 0.45 to 0.4 0.1 by 0.1, and find that both precision and recall enhance.

[glenn-jocher]

Hello!

Thank you for your question and for sharing your observations. The behavior you're seeing is related to how the Intersection over Union (IoU) threshold affects the calculation of the confusion matrix in object detection tasks.

How the Confusion Matrix Works in Object Detection

The confusion matrix for object detection is used to evaluate the performance of a model by comparing the predicted bounding boxes to the ground truth bounding boxes. It categorizes the predictions into True Positives (TP), False Positives (FP), and False Negatives (FN) based on the IoU threshold.

Here's a brief overview of the process:

1. IoU Calculation: For each predicted bounding box, the IoU with each ground truth bounding box is calculated. IoU is a measure of the overlap between two bounding boxes.

2. Thresholding: Predictions are considered True Positives if their IoU with a ground truth box exceeds the specified threshold (iou_thres). If the IoU is below the threshold, the prediction is considered a False Positive. Ground truth boxes that do not have a corresponding prediction with IoU above the threshold are considered False Negatives.

3. Updating the Confusion Matrix: Based on the above classifications, the confusion matrix is updated to reflect the counts of TP, FP, and FN.

Impact of Changing IoU Threshold

When you lower the IoU threshold, you are essentially making it easier for a predicted bounding box to be considered a True Positive. This can lead to:

• Increased Precision: More predicted boxes are classified as True Positives, reducing the number of False Positives.
• Increased Recall: More ground truth boxes are matched with predicted boxes, reducing the number of False Negatives.

This explains why both precision and recall improve when you decrease the IoU threshold.

Example Code

Here's a simplified example of how the confusion matrix is updated in the process_batch method of the ConfusionMatrix class:

def process_batch(self, detections, gt_bboxes, gt_cls):
    if gt_cls.shape[0] == 0:  # Check if labels are empty
        if detections is not None:
            detections = detections[detections[:, 4] > self.conf]
            detection_classes = detections[:, 5].int()
            for dc in detection_classes:
                self.matrix[dc, self.nc] += 1  # false positives
        return
    if detections is None:
        gt_classes = gt_cls.int()
        for gc in gt_classes:
            self.matrix[self.nc, gc] += 1  # background FN
        return

    detections = detections[detections[:, 4] > self.conf]
    gt_classes = gt_cls.int()
    detection_classes = detections[:, 5].int()
    iou = box_iou(gt_bboxes, detections[:, :4])

    x = torch.where(iou > self.iou_thres)
    if x[0].shape[0]:
        matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
        if x[0].shape[0] > 1:
            matches = matches[matches[:, 2].argsort()[::-1]]
            matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
            matches = matches[matches[:, 2].argsort()[::-1]]
            matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
    else:
        matches = np.zeros((0, 3))

    n = matches.shape[0] > 0
    m0, m1, _ = matches.transpose().astype(int)
    for i, gc in enumerate(gt_classes):
        j = m0 == i
        if n and sum(j) == 1:
            self.matrix[detection_classes[m1[j]], gc] += 1  # correct
        else:
            self.matrix[self.nc, gc] += 1  # true background

    if n:
        for i, dc in enumerate(detection_classes):
            if not any(m1 == i):
                self.matrix[dc, self.nc] += 1  # predicted background

Conclusion

Adjusting the IoU threshold can significantly impact the performance metrics of your object detection model. Lowering the threshold generally increases both precision and recall, as it makes it easier for predictions to be considered correct.

If you have any more questions or need further clarification, feel free to ask! 😊

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