The official pytorch implementation of SSP: A Large-Scale Semi-Real Dataset for Semantic Segmentation of Spacecraft Payloads.
SPSNet is used for the segmentation of spacecraft payloads which an anti-pyramid-structure decoder.
We test our code in Python 3.7, CUDA 11.1, cuDNN 8, and PyTorch 1.7.1. We provide Dockerfile to build the docker image we used. You can modify the Dockerfile as you want.
You can download our release dataset SSP from Here with password rv25. The SSP dataset is the first dataset with semi-real images for the segmentation of spacecraft payloads.
The pre-trained weights of SPSNet can be downloaded from here.
- Clone this repo
$ git clone https://github.com/Dr-zfeng/SPSNet.git
- Build docker image
$ cd ~/SPSNet
$ docker build -t docker_image_spsnet .
- Download the dataset
$ (You should be in the SPSNet folder)
$ mkdir ./dataset
$ cd ./dataset
$ (download our preprocessed dataset.zip in this folder)
$ unzip -d .. dataset.zip
- To reproduce our results, you need to download our pre-trained weights.
$ (You should be in the SPSNet folder)
$ mkdir ./weights_backup
$ cd ./weights_backup
$ (download our preprocessed weights.zip in this folder)
$ unzip -d .. weights.zip
$ docker run -it --shm-size 8G -p 1234:6006 --name docker_container_spsnet --gpus all -v ~/SPSNet:/workspace docker_image_spsnet
$ (currently, you should be in the docker)
$ cd /workspace
$ python3 run_demo.py
The results will be saved in the ./runs folder.
- To train SPSNet
$ (You should be in the SPSNet folder)
$ docker run -it --shm-size 8G -p 1234:6006 --name docker_container_spsnet --gpus all -v ~/SPSNet:/workspace docker_image_spsnet
$ (currently, you should be in the docker)
$ cd /workspace
$ python3 train_student.py
- To see the training process
$ (fire up another terminal)
$ docker exec -it docker_container_spsnet /bin/bash
$ cd /workspace
$ tensorboard --bind_all --logdir=./runs/tensorboard_log/
$ (fire up your favorite browser with http://localhost:1234, you will see the tensorboard)
The results will be saved in the ./runs folder.
Note: Please change the smoothing factor in the Tensorboard webpage to 0.999, otherwise, you may not find the patterns from the noisy plots. If you have the error docker: Error response from daemon: could not select device driver, please first install NVIDIA Container Toolkit on your computer!
If you use SPSNet in your academic work, please cite:
Some of the codes are borrowed from MAFNet
Contact: zfeng94@outlook.com