Academic reference

• Essay of Project

Environment

• we worked over TensorFlow 1.4 and anaconda environment. a .yml file with dependencies will be uploaded soon.

Hands on

This bullets are consecutive steps we suggest to perform for easier diving-in:

• First, we are available for any question and support here and on asheryartsev@gmail.com - so don't be shy.
• focus on cucu_train.py only. read the code that deals with creating the model and relevant data. it is self explanatory.
• we bring to notice that many sections are stiil to-be-extracted to methods etc.
• Run it and handle all env. obstacles which will get to you:)
• now, once you are ready to play with all the parameters of our project open cucu_config.py - you got there anything you need to control NN hyper-parameters and data-generating parameters.
  • we suggest to nevigate to original config file of Mask RCNN where hyperparameters definitions are more elaborated.
• Next we suggest to explore our project_assets folder.
  • There, you'll get aqcuainted with our classes for generated dataset creation, real, and hybrid (cucu_classes.py).
  • In cucu_utils.py we poured core-functions for generating synthetic images of crops.
• Now, you should be ready for playground.py where you can run different metrics on a small test-set to benchmark your trained NN.
• Lot's of analysing functionalities where imported and upgraded from here