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https://github.com/PavlosMelissinos/enet-keras

A keras implementation of ENet (abandoned for the foreseeable future)
https://github.com/PavlosMelissinos/enet-keras

Last synced: about 2 months ago
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A keras implementation of ENet (abandoned for the foreseeable future)

Host: GitHub
URL: https://github.com/PavlosMelissinos/enet-keras
Owner: PavlosMelissinos
License: mit
Created: 2017-03-30T19:55:12.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2022-12-07T23:36:33.000Z (almost 2 years ago)
Last Synced: 2024-07-10T11:01:42.100Z (3 months ago)
Language: Python
Homepage:
Size: 282 KB
Stars: 115
Watchers: 16
Forks: 46
Open Issues: 18
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        # ENet-keras

[![license](https://img.shields.io/github/license/mashape/apistatus.svg)](https://github.com/PavlosMelissinos/enet-keras/blob/master/LICENSE)

![](https://reposs.herokuapp.com/?path=PavlosMelissinos/enet-keras&style=flat&color=red)

[![Read the Docs](https://img.shields.io/readthedocs/pip.svg)]()

This is an implementation of [ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://arxiv.org/abs/1606.02147), ported from [ENet-training](https://github.com/e-lab/ENet-training) ([lua-torch](https://github.com/torch/torch7)) to [keras](https://github.com/fchollet/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](https://github.com/PavlosMelissinos/enet-keras/issues/11)

  - [ ] Fix MSCOCOReduced, [also see #9](https://github.com/PavlosMelissinos/enet-keras/issues/9)

  - [ ] ?????