ENet-keras

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This is an implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, ported from ENet-training (lua-torch) to keras.

Installation

Get code

git clone https://github.com/PavlosMelissinos/enet-keras.git
cd enet-keras

Setup environment

Dependencies

On poetry: poetry install

On Anaconda/miniconda: conda env create -f environment.yml

On pip: pip install -r requirements.txt

Set up data/model

make setup

The setup script only sets up some directories and converts the model to an appropriate format.

Usage

Train on MS-COCO

make train

Remaining tasks

• [ ] Clean up code
  • [ ] Remove hardcoded paths
  • [ ] Add documentation everywhere
• [ ] Test code
  • [ ] Add tests
• [ ] Fix performance (mostly preprocessing bottleneck)
  • [ ] Remove unnecessary computations in data preprocessing
  • [ ] Index dataset category internals. Dataset categories have fields with one-to-one correspondence like id, category_id, palette, categories. This seems like perfect table structure. Might be too much though.
  • [ ] (Optionally) Make data loader multithreaded (no idea how to approach this one, multithreadedness is handled by keras though)
• [ ] Enhance reproducibility/usability
  • [x] Upload pretrained model
  • [ ] Finalize predict.py
  • [x] Test whether it works after latest changes
  • [ ] Modify predict.py to load a single image or from a file. There's no point in loading images from the validation set.
• [ ] Fix bugs
  • [ ] Investigate reason for bad results, see #11
  • [ ] Fix MSCOCOReduced, also see #9
  • [ ] ?????