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FuseNet implementation in PyTorch

This is the PyTorch implementation for FuseNet, developed based on Pix2Pix code.

Prerequisites

  • Linux
  • Python 3.7.0
  • CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

git clone https://github.com/MehmetAygun/fusenet-pytorch
cd fusenet-pytorch
pip install -r requirements.txt

Dataset preparation

sunrgbd dataset

nyuv2 dataset

  • Download the dataset and create the training set
cd datasets
sh download_nyuv2.sh
python create_training_set.py

scannetv2 dataset

  • Download the scannet_frames_25k and scannet_frames_test under /datasets/scannet/tasks/

FuseNet train/test

visdom visualization

  • To view training errors and loss plots, set --display_id 1, run python -m visdom.server and click the URL http://localhost:8097
  • Checkpoints are saved under ./checkpoints/sunrgbd/

train & test on sunrgbd

python train.py --dataroot datasets/sunrgbd --dataset sunrgbd --name sunrgbd

python test.py --dataroot datasets/sunrgbd --dataset sunrgbd --name sunrgbd --epoch 400

train & test on nyuv2

python train.py --dataroot datasets/nyuv2 --dataset nyuv2 --name nyuv2

python test.py --dataroot datasets/nyuv2 --dataset nyuv2 --name nyuv2 --epoch 400

train & val & test on scannetv2

python train.py --dataroot datasets/scannet/tasks/scannet_frames_25k --dataset scannetv2 \
                --name scannetv2

python test.py --dataroot datasets/scannet/tasks/scannet_frames_25k --dataset scannetv2 \
               --name scannetv2 --epoch 380 --phase val

python test.py --dataroot datasets/scannet/tasks/scannet_frames_test --dataset scannetv2 \
               --name scannetv2 --epoch 380 --phase test

Results

  • We use the training scheme defined in FuseNet
  • Loss is weighted for SUNRGBD dataset
  • Learning rate is set to 0.01 for NYUv2 dataset
  • Results can be improved with a hyper-parameter search
  • Results on the scannetv2-test (w/o class-weighted loss) can be found here
Dataset FuseNet-SF5 (CAFFE) FuseNet-SF5
overall mean iou overall mean iou
sunrgbd 76.30 48.30 37.30 75.41 46.48 35.69
nyuv2 66.00 43.40 32.70 68.76 46.42 35.48
scannetv2-val -- -- -- 76.32 55.84 44.12
scannetv2-cls_weighted-val -- -- -- 76.26 55.74 44.40
scannetv2-test avg iou bathtub bed bookshelf cabinet chair counter curtain desk door floor other furniture picture refrigerator shower curtain sink sofa table toilet wall window
no-cls_weighted 52.1 59.1 68.2 22.0 48.8 27.9 34.4 61.0 46.1 47.5 91.0 29.3 44.7 51.2 39.7 61.8 56.7 45.2 73.4 78.2 56.6
cls_weighted 53.5 57.0 68.1 18.2 51.2 29.0 43.1 65.9 50.4 49.5 90.3 30.8 42.8 52.3 36.5 67.6 62.1 47.0 76.2 77.9 54.1

Citation

@inproceedings{hazirbas16fusenet,
  Title                    = {{FuseNet}: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture},
  Author                   = {Hazirbas, Caner and Ma, Lingni and Domokos, Csaba and Cremers, Daniel},
  Booktitle                = {Asian Conference on Computer Vision ({ACCV})},
  Year                     = {2016},
  Doi                      = {10.1007/978-3-319-54181-5_14},
  Url                      = {https://github.com/tum-vision/fusenet}
}

Acknowledgments

Code is inspired by pytorch-CycleGAN-and-pix2pix.

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