Added a run.py file that uses RedNet prediction over a dataset named "mestrado", subdivided into four sets.
This file also collect some statistics at run. Added "only cpu" option.
Added Dockerfile (builded with name "obeach") and docker-compose.yml with a image with compatible system requirements to run this project.
Results obtained with "mestrado" dataset will be public available soon, comparing this approach with others semantic sgmentation algorithms.
This repository use the official implementation of the RedNet (Residual Encoder-Decoder Architecture). It turns out that the simple encoder-decoder structure is powerful when combined with residual learning. For further details of the network, please refer to our article RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation.
PyTorch 0.4.0, TensorboardX 1.2 and other packages listed in requirements.txt
.
The RedNet model is trained and evaluated with the SUN RGB-D Benchmark suit. Please download the data on the official webpage, unzip it, and place it with a folder tree like this,
SOMEPATH # Some arbitrary path
├── SUNRGBD # The unzip folder of SUNRGBD.zip
└── SUNRGBDtoolbox # The unzip folder of SUNRGBDtoolbox.zip
The root path SOMEPATH
should be passed to the program using the --data-dir SOMEPATH
argument.
For training, you can pass the following argument,
python RedNet_train.py --cuda --data-dir /path/to/SOMEPATH
If you do not have enough GPU memory, you can pass the --checkpoint
option to enable the checkpoint container in PyTorch >= 0.4. For other configuration, such as batch size and learning rate, please check the ArgumentParser in RedNet_train.py.
For inference, you should run the RedNet_inference.py like this,
python RedNet_inference.py --cuda --last-ckpt /path/to/pretrained/model.pth -r /path/to/rgb.png -d /path/to/depth.png -o /path/to/output.png
The pre-trained weight is released here for result reproduction.
If you find this work to be helpful, please consider citing the paper,
@article{jiang2018rednet,
title={RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation},
author={Jiang, Jindong and Zheng, Lunan and Luo, Fei and Zhang, Zhijun},
journal={arXiv preprint arXiv:1806.01054},
year={2018}
}
This software is released under a creative commons license which allows for personal and research use only. For a commercial license please contact the authors. You can view a license summary here: http://creativecommons.org/licenses/by-nc/4.0/