<p>You can train a model with that dataset, but it will perform worse compared to a higher fidelity dataset. Training on images with unannotated buildings is essentially instructing the model to stochastically ignore buildings, which is confusing.</p>

CC BY-SA 4.0
2023-10-13T20:29:11.403Z
2023-10-13T20:29:11.403Z
Karl
<p>I am wondering if it is required or not that the masks used for binary instance segmentation are complete.</p>
<p>For instance, I want to find the buildings in aerial imagery. If my masks cover, let say between 25% and 75% of the training image buildings, can these samples be used for training the model? Does the masks should cover 100% of the buildings in the samples?</p>
<p>I know it would be better to mask all buildings, but if the mask is not complete, can it be used for training or not?</p>
<p>I am using a deeplabv3 model.</p>
<p>Here an example of what I mean
<a href="https://i.sstatic.net/MMMOg.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/MMMOg.png" alt="enter image description here" /></a></p>

<image-segmentation><instance-segmentation>
Binary instance segmentation - does the masks have to be complete
Below the Radar
# Binary instance segmentation - does the masks have to be complete

(Asked by: username_0 on 2023-10-12)

I am wondering if it is required or not that the masks used for binary instance segmentation are complete.

For instance, I want to find the buildings in aerial imagery. If my masks cover, let say between 25% and 75% of the training image buildings, can these samples be used for training the model? Does the masks should cover 100% of the buildings in the samples?

I know it would be better to mask all buildings, but if the mask is not complete, can it be used for training or not?

I am using a deeplabv3 model.

Here an example of what I mean
[![enter image description here](https://i.sstatic.net/MMMOg.png)](https://i.sstatic.net/MMMOg.png)

**Tags:** image-segmentation, instance-segmentation


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## 1 answers

### Answer by username_1 on 2023-10-13. Score: 1

You can train a model with that dataset, but it will perform worse compared to a higher fidelity dataset. Training on images with unannotated buildings is essentially instructing the model to stochastically ignore buildings, which is confusing.


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