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I have one small dataset which contains both bounding box and segmentation mask. And another larger dataset that has only bounding box ground truths. And my objective is to create a object detection model ( I am planning to use yolo-v7 but I feel like the main logic is very similar to yolo-v5 especially the segmentation part?).
Is it possible to utilize both datasets? my thoughts:
Idea 1. modify the segmentation dataloader, loss calculations - so that we use masks and do masks loss only if masks exist.
Idea 2. minimal changes to the code. Just train with segmentation data for about 10 epochs, save off checkpoint, then load checkpoint, switch to object detection model/data , train for 10 epochs, then switch back to segmentation data.
Seeking some helps and suggestions about:
Has anyone already did this somewhere I could learn from?
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Hi,
I have one small dataset which contains both bounding box and segmentation mask. And another larger dataset that has only bounding box ground truths. And my objective is to create a object detection model ( I am planning to use yolo-v7 but I feel like the main logic is very similar to yolo-v5 especially the segmentation part?).
Is it possible to utilize both datasets? my thoughts:
Idea 1. modify the segmentation dataloader, loss calculations - so that we use masks and do masks loss only if masks exist.
Idea 2. minimal changes to the code. Just train with segmentation data for about 10 epochs, save off checkpoint, then load checkpoint, switch to object detection model/data , train for 10 epochs, then switch back to segmentation data.
Seeking some helps and suggestions about:
Thanks a lot!!
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