Trying to Customize The VOC2012 semantic segmentation RESNET Algorithm to detect solar panels in aerial imagery

1

Can anyone help answer the question of if it is possible to customisze the semantic segmentstion example on AWS sagemaker to detect solar panels in aerial imagery? I have validation and test dataset and have annotated both also. I was wondering if I can add a new class for the solar panel and do a binary segmentation of solar pixels is = 1 and background = 0 using the sagemaker jupyter notebook example FCN algorthim on sagemaker. I am new to python. I followed the example using my own datat set but got the below error

“CustomerError: Unable to open label file img00000057.png”
" raise CustomerError('Unable to open label file ’ + mask_file_name)"

2

This error seems to indicate that the path to your image is not correct. You can maybe try to follow this tutorial if you find it easier:
https://gluon-cv.mxnet.io/build/examples_segmentation/train_psp.html

or this one:
https://gluon-cv.mxnet.io/build/examples_segmentation/train_fcn.html