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

Install all requirements, as listed below
Download mscoco annotations (see below)
Download and convert graph weights with HeplerScripts/CreateVggGraphWeights.py (see below)
Create the learning dataset using ExamplesGenerator.py
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)
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

abbypa/NNProject_DeepMask

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