title: Which parameters of Mask-RCNN control mask recall?
body: <p>I'm interested in fine-tuning a Mask-RCNN model that I'm using for instance segmentation. Currently I have trained the model for 6 epochs and the various Mask-RCNN losses are as follows:</p>
  <p><a href="https://i.stack.imgur.com/vYUz6.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/vYUz6.png" alt="enter image description here" /></a></p>
  <p>The reason I'm stopping is that the COCO evaluation metrics seem to have dipped in the last epoch:</p>
  <p><a href="https://i.stack.imgur.com/rxxUo.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/rxxUo.png" alt="enter image description here" /></a></p>
  <p>I know this is a far reaching question, but I'm looking to gain some intuition of how to understand which parameters are going to be the most impactful in improving the evaluation metrics. I understand there are three places to consider:</p>
  <ol>
  <li>Should I be looking at batch size, learning rate and momentum, this uses an SGD optimizer with a learning rate of 1e-4 and batch size 2?</li>
  <li>Should I be looking at using more training data or adding augmentation (I don't currently use any) and my dataset is current pretty large 40K images?</li>
  <li>Should I be looking at the specific MaskRCNN parameters?</li>
  </ol>
  <p>I thing I'll likely be asked to me more specific on what I want to improve so let me say that I would like to improve the recall of the individual masks. The model is performing well but doesn't quite capture the full extend of what I would like it to. I'm also leaving out details of the specific learning problem as I'd like to gain intuition of how to approach this in general.</p>