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.

• Python
  • Anaconda for Windows x64 with Python 2.7 (Anaconda version 2.4.1)
• Theano (0.8.0.dev0)
  • conda install mingw libpython
  • git clone https://github.com/Theano/Theano.git
  • python setup.py install
• Keras (0.3.1)
  • NOTICE- if you get an error regarding the 'Graph' object, you are probably not using the correct Keras version
• Open CV (3.1.0)
  • Download installation here
  • Copy cv2.pyd to site-packages dir inside python's lib dir in anaconda
• Coco API (1.0.1)
  • git clone https://github.com/pdollar/coco
  • python setup.py build_ext install

Required downloads

• VGG-D
  • Net was taken from here. Weights should be downloaded locally, and converted to graph format using HelperScripts/CreateVggGraphWeights.py
  • Full article: Very Deep Convolutional Networks for Large-Scale Image Recognition
• MSCOCO
  • Download annotation files here