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First, you need to check, if the annotations on your validation images are correct and complete (i.e. whether all target instances have been annotated). If annotations are not correct and complete, then you may have omitted annotations in your training images. This can be the reason for the low Recall-value, as the application will also mimic your annotation behavior and omit instances.
Further, you can add more annotated regions with the label of your interest to the training set to improve your performance. You will get the optimal improvements by adding images or ROI(s) containing instances with a similar appearance to the validation images that have not been correctly classified by the trained model.
If annotations are correct and complete, then you can improve your algorithm app by adding new images or ROI(s) containing annotations to the training set. You will get the optimal improvements by adding images or ROI(s) containing instances with a similar appearance to the regions in the validation images that have not been correctly classified by the trained model.
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