<p><code>Simple Copy Paste</code> is a strong method for data augmentation for instance segmentation related tasks.</p>
<p>Check about the research paper <a href="https://arxiv.org/abs/2012.07177" rel="nofollow noreferrer">here</a>.</p>
<p>For unofficial github code, check <a href="https://github.com/conradry/copy-paste-aug" rel="nofollow noreferrer">here</a></p>
<p><code>Albumentation</code> and <code>TorMentor</code> are also useful libraries for data augmentation.</p>
What is the best way to create augmentation on image dataset while training instance segmentation?
<p>Background : I am using YOLACT instance segmentation model to train set of images. The dataset size is very small (~20 images). The model doesn't converge properly (of-course given the dataset size). I wanted to increase the dataset size by adding some augmented images. I know we have various image augmentation techniques and packages like imgaug , albumentation, opencv etc. but I need image &amp; annotation file ( COCO JSON ) format to train the model.</p>
<p>So my question is :</p>
<p>Is there a package that helps me to automatically generate the annotations of augmented images ?</p>
<p>or</p>
<p>Is there a better way to address my issue ?</p>
<p>Thank you in advance for your help!</p>