Deep Neural Network for object segmentation.
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HelperScripts script for reading random test and train data Apr 24, 2016
Trials file order Apr 22, 2016 File for project constants, Apr 19, 2016 fixed max threshold, Apr 23, 2016 minor leftovers May 1, 2016 Caching train & test datasets Apr 24, 2016 file order Apr 22, 2016 no img resizing by default Apr 23, 2016 File for project constants, Apr 19, 2016 Update Nov 16, 2016

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/ (see below)
  4. Create the learning dataset using
  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
  6. Run

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