Fine tuning Mask RCNN results

Yuliang_Zhu: <p>Hi,</p>
<p>I used the fine tune mask rcnn tutorial to train a model to differentiate weeds from crops using the CFWID dataset (available on github), and the results look a little bit wired, even though I am aware that the number of training samples in the dataset is far from enough for the model to work (60 in my case). <img src="https://discuss.pytorch.org/uploads/default/original/3X/2/5/258b8b40dffbeb49bc89917244f974e59a89751c.png" alt="Screenshot from 2020-11-25 22-20-59" data-base62-sha1="5m8DyjxCiDd9MQNFkgALL3TQGQI" width="649" height="342"> <div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/3/6/36c0ca0b2d582c29a5b965b117d87576db5f9d0e.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/36c0ca0b2d582c29a5b965b117d87576db5f9d0e" title="007_pred"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/3/6/36c0ca0b2d582c29a5b965b117d87576db5f9d0e_2_625x500.jpeg" alt="007_pred" data-base62-sha1="7OmPBRDz1uJVtUjysBlm8OqtaLc" width="625" height="500" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/3/6/36c0ca0b2d582c29a5b965b117d87576db5f9d0e_2_625x500.jpeg, https://discuss.pytorch.org/uploads/default/original/3X/3/6/36c0ca0b2d582c29a5b965b117d87576db5f9d0e.jpeg 1.5x, https://discuss.pytorch.org/uploads/default/original/3X/3/6/36c0ca0b2d582c29a5b965b117d87576db5f9d0e.jpeg 2x" data-dominant-color="8C8F8A"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">007_pred</span><span class="informations">720×576 94.9 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div> <div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/b/5/b51f98e59659ef8bd922e4d0fe7c06746c88c431.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/b51f98e59659ef8bd922e4d0fe7c06746c88c431" title="009_pred"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/b/5/b51f98e59659ef8bd922e4d0fe7c06746c88c431_2_625x500.jpeg" alt="009_pred" data-base62-sha1="pQi7ng5I7Haft0HjUyMnba6xz9v" width="625" height="500" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/b/5/b51f98e59659ef8bd922e4d0fe7c06746c88c431_2_625x500.jpeg, https://discuss.pytorch.org/uploads/default/original/3X/b/5/b51f98e59659ef8bd922e4d0fe7c06746c88c431.jpeg 1.5x, https://discuss.pytorch.org/uploads/default/original/3X/b/5/b51f98e59659ef8bd922e4d0fe7c06746c88c431.jpeg 2x" data-dominant-color="8C8D89"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">009_pred</span><span class="informations">720×576 101 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div> <div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/d/a/dae4f9c016ee44003792d40ca30a667b99a82d2e.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/dae4f9c016ee44003792d40ca30a667b99a82d2e" title="012_pred"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/d/a/dae4f9c016ee44003792d40ca30a667b99a82d2e_2_625x500.jpeg" alt="012_pred" data-base62-sha1="veqFjZvuIofIDTJ43il432S2BjM" width="625" height="500" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/d/a/dae4f9c016ee44003792d40ca30a667b99a82d2e_2_625x500.jpeg, https://discuss.pytorch.org/uploads/default/original/3X/d/a/dae4f9c016ee44003792d40ca30a667b99a82d2e.jpeg 1.5x, https://discuss.pytorch.org/uploads/default/original/3X/d/a/dae4f9c016ee44003792d40ca30a667b99a82d2e.jpeg 2x" data-dominant-color="8C8C8B"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">012_pred</span><span class="informations">720×576 91.2 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div><br>
The model predicts some regions as both weeds and crops, and these regions have large overlap (IoU). I was wondering if there is any mistake I made in the data format processing? Does mask rcnn perform some non-maximal suppression to avoid such predtctions? Thank you in advance.</p>