Closed samf1986 closed 3 years ago
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@samf1986 I don't understand your question, but in general yes, incorrect labels will negatively affect your training results.
❔Question
I'm trying to implement pseudo-labeling with yolov5 by predicting labels for unlabeled data at a high confidence level (e.g. 0.9). However most of my images have multiple true positives, so I'm concerned that if I add this "student" data as is, I will be adding many false negatives to the training set. I haven't found any resources addressing this issue, so my provision solution was to black out all regions of the images not within a pseudo-label bounding box before adding the images to the training set.
Is this correct? Do false negatives in the training set actually cause problems in Yolov5?
I have same interest in pseudo-labeling, do you have any progress up to now?
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
❔Question
I'm trying to implement pseudo-labeling with yolov5 by predicting labels for unlabeled data at a high confidence level (e.g. 0.9). However most of my images have multiple true positives, so I'm concerned that if I add this "student" data as is, I will be adding many false negatives to the training set. I haven't found any resources addressing this issue, so my provision solution was to black out all regions of the images not within a pseudo-label bounding box before adding the images to the training set.
Is this correct? Do false negatives in the training set actually cause problems in Yolov5?