This is a Keras-based Python implementation of DeepMask- a complex deep neural network for learning object segmentation masks

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Deep LearningNNProject_DeepMask
Overview

NNProject - DeepMask

This is a Keras-based Python implementation of DeepMask- a complex deep neural network for learning object segmentation masks. The full article can be found here: Learning to Segment Object Candidates.

This was implemented as a final project for TAU Deep Learning course (2016).

General instructions

  1. Install all requirements, as listed below
  2. Download mscoco annotations (see below)
  3. Download and convert graph weights with HeplerScripts/CreateVggGraphWeights.py (see below)
  4. Create the learning dataset using ExamplesGenerator.py
  5. Create a train and test directories with examples to train and test on. Default locations are 'Predictions/train' and same for test (can be configured in EndToEnd.py)
  6. Run EndToEnd.py

Required installations

This was run on Windows 8.1 (64 bit) on a CPU with 8GB RAM. In brackets are the versions I used.

Required downloads

Owner
GitHub Repository
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