This README contains a lot of information from the original RedNet repository.
PyTorch 0.4.0, TensorboardX 1.2 and other packages listed in requirements.txt. Additionally, tqdm is also used and can be installed using pip install tqdm.
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.zipThe 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.
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/