ShelfNet-lightweight for paper (ShelfNet for fast semantic segmentation)

• This repo contains implementation of ShelfNet-lightweight models for real-time models on Cityscapes.
  
• For real-time tasks, we achieved 74.8% mIoU on Ctiyscapes dataset, with a speed of 59.2 FPS (61.7 FPS for BiSeNet at 74.7% on a GTX 1080Ti GPU).
  
• For non real-time tasks, we achieved 79.0% mIoU on Cityscapes test set with ResNet34 backbone, suparssing other models (PSPNet and BiSeNet) with largers backbones with ResNet50 or Resnet 101 backbone.
  
• For Non light-weight ShelfNet implementation, refer to another ShelfNet repo.
  
• This branch is the result on Cityscapes experiment, for results on PASCAL, see branch pascal

This repo is based on two implementations Implementation 1 and Implementation 2. This implementation takes about 24h's training on 2 GTX 1080Ti GPU.

Results

Link to results on Cityscapes test set

ShelfNet18-lw real-time: https://www.cityscapes-dataset.com/anonymous-results/?id=b2cc8f49fc3267c73e6bb686425016cb152c8bc34fc09ac207c81749f329dc8d
ShelfNet34-lw non real-time: https://www.cityscapes-dataset.com/anonymous-results/?id=c0a7c8a4b64a880a715632c6a28b116d239096b63b5d14f5042c8b3280a7169d

Data Preparation

Download fine labelled dataset from Cityscapes server, and decompress into ./data folder.
You might need to modify data path here and here

$ mkdir -p data
$ mv /path/to/leftImg8bit_trainvaltest.zip data
$ mv /path/to/gtFine_trainvaltest.zip data
$ cd data
$ unzip leftImg8bit_trainvaltest.zip
$ unzip gtFine_trainvaltest.zip

Two models and the pretrained weights

We provide two models, ShelfNet18 with 64 base channels for real-time semantic segmentation, and ShelfNet34 with 128 base channels for non-real-time semantic segmentation.
Pretrained weights for ShelfNet18 and ShelfNet34.

Requirements

PyTorch 1.1
python3
scikit-image
tqdm

How to run

Find the folder (cd ShelfNet18_realtime or cd ShelfNet34_non_realtime)

training

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py

evaluate on validation set (Create a folder called res, this folder is automatically created if you train the model. Put checkpoint in resfolder, and make sure the checkpoint name and dataset path match evaluate.py. Change checkpoint name to model_final.pthby default)

python evaluate.py

Running speed

test running speed of ShelfNet18-lw

python test_speed.py

You can modify the shape of input images to test running speed, by modifying here
You can test running speed of different models by modifying here
The running speed is an average of 100 single forward passes, therefore it's possible the speed varies. The code returns the mean running time by default.