Python-Keras实现实时语义分割的深层神经网络架构ENET

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  • 2022-04-01 19:06
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Keras实现实时语义分割的深层神经网络架构ENET
Python-Keras实现实时语义分割的深层神经网络架构ENET.zip
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# 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 ### Setup environment #### Dependencies On Anaconda/miniconda: `conda env create -f environment.yml` On pip: `pip install -r requirements.txt` #### One-time dependencies `pip install Cython` in order to make pycocotools. `pip install torchfile` in order to convert the torch model to a keras one. #### Build pycocotools ``` cd src/data/pycocotools/ make ``` ### Get code `git clone https://github.com/PavlosMelissinos/enet-keras.git` ### Set up data/model ``` cd enet-keras ./setup.sh ``` The setup script only sets up some directories and converts the model to an appropriate format. MSCOCO is only downloaded on demand. ## Usage ### Train on MS-COCO `./train.sh` ## 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) - [ ] ?????
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