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SegNet-Semantic-Segmentation

Implementing: https://arxiv.org/pdf/1511.00561.pdf

Prerequisite:

  • Tensorflow 2.0
  • Numpy
  • Keras
  • Pandas

Dataset:

  • CamVid dataset, which contains real time scenes and their annotated images.

Architecture:

There are no fully connected layers and hence it is only convolutional. A decoder upsamples its input using the transferred pool indices from its encoder to produce a sparse feature map(s). It then performs convolution with a trainable filter bank to densify the feature map. The final decoder output feature maps are fed to a soft-max classifier for pixel-wise classification.

Result:

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Deep Convolutional Encoder-Decoder network for image segmentation

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