False Positives in Instance Segmentation

I did the mmdetection tutorial on training Object detection on a balloon dataset.

Only changed it to train an Instance Segemtation model. The results are kind of nice, the network gets a good concept on what a balloon is on only \~60 example images. I then tried inference on the original demo image that contains a bench, and I get false positives of objects from the original COCO dataset (low probability but still).


Are there strategies to mitigate this behavior? …

computervision concept dataset demo detection example false false positives good image images inference instance kind network nice segmentation train training tutorial