Segmentation for CityScapes dataset
Straightforward implementation of Segmentation for CityScapes dataset
Description
First, you can go to the official site(https://www.cityscapes-dataset.com/downloads/).
Then, download leftImg8bit_trainvaltest.zip(11GB) and gtFine_trainvaltest.zip(241MB). Make sure that you've registered your email on this site before downloading and that email is not a gmail account.
I personally used 2975 images as train data, 500 images as both validation and test data. I saved configuration details in files located at Csv dir.
Pyramid Scene Parsing Network (PSPNet) is the model architecture proposed by this paper(https://arxiv.org/abs/1612.01105).
Input Image(left), Output Image(center), Ground Truth(right)
Usage
generate the pickle file contaning hyperparameter values by running the following command.
python config.py (-h)
you will see the pickle file in Pkl dir.
now you can start training the model.
python train.py -p Pkl/***.pkl
After training is done, you could see prediction of pretrained model
python infer.py -p Weights/***.pt
Dependencies
- Python = 3.6.10
- PyTorch = 1.6.0
- OpenCV (pip install opencv-python)
- numpy
- matplotlib (only for test)
Environment
I leave my own environment below. I tested it out on a single GPU.
PSPNet is a kind of large model architecture, so the bigger GPU memory is desirable for training.
- OS:
- Linux(Ubuntu 18.04.5 LTS)
- GPU:
- NVIDIA® GeForce® RTX 2080 Ti VENTUS 11GB OC
- CPU:
- Intel® Xeon® CPU E5640 @ 2.67GHz