Project progress:
Semantic image segmentation for Drone images.
We planned to compare the results of two image segmentation models.
1. Fully Convolutional Network (FCN)
For the encoder part of this network, VGG-16 is implemented.
The convolutional block is defined as,
We loaded the encoder network with pretrained weights from VGG-16
model.
These layers are left trainable, so the model will update these weights
during training.
For the decoder part, FCN-8 is implemented. Like this
In colab,
Model summary,
During training, 80%+ accuracy was achieved after only 6 epochs.
Although, the model is very slow in terms of training. So, reducing the
images to a smaller dimension and freezing the pretrained weights
might reduce computational complexity.
After this we will try to train the same dataset on U-Net architecture to
compare the results. For comparison, IoU and Dice scores should be
appropriate.