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Semantic-Segmentation-with-ENet

Tests

Tensorflow 2.0 Implementation of the E-Net Semantic Segmentation Architecture

Based on the paper "ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation"

Setup

  • Clone the repository
  • Install the package locally using the following command
pip install -e Semantic-Segmentation-with-Enet/

Files in the Repository

  • tests/tests_enet.py : Python script to test the dimensions of all the blocks and model using random data

  • evaluation/dataset_prep.py : Prepares the training data and decodes & encodes the segmented images in the dataset

  • enet_seg/enet_blocks.py : Definitions of the Bottleneck layers in the architecture

  • enet_seg/enet_model.py : Final model including all the layers outlined in the paper

  • enet_seg/utilities.py : Contains the definition of the Max Unpool layer