My implementation of Deeplab_v3plus. This repository is based on the dataset of cityscapes and the mIOU is 70.54.
I am working with python3.5 and pytorch1.0.0 built from source. Other environments are not tested, but you need at least pytorch1.0 since I use torch.distributed to manipulate my gpus. I use two 1080ti to train my model, so you also need two gpus each of which should have at least 9G memory.
The experiment is done with the dataset of CityScapes. You need to register on the website and download the dataset images and annotations. Then you create a data directory and then decompress.
$ cd DeepLabv3plus
$ mkdir -p data
$ mv /path/to/leftImg8bit_trainvaltest.zip data
$ mv /path/to/gtFine_trainvaltest.zip data
$ cd data
$ unzip leftImg8bit_trainvaltest.zip
$ unzip gtFine_trainvaltest.zip
After creating the dataset, you can train on the cityscapes train set and evaluate on the validation set.
Train:
$ cd DeepLabv3plus
$ CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py
This will take around 13 hours on two 1080ti gpus. After training, the model will be evaluated on the val set automatically, and you will see a mIOU of 70.54.
Eval: If you want to evaluate a trained model, you can also do this:
$ python evaluate.py
or if you want to evaluate on multi-gpus, you can also do this:
$ CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 evaluate.py
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If you want to use your own dataset, you may implement you
datasetfile as does with mycityscapes.py. -
As for the hyper-parameters, you may change them in the configuration file
configs/configurations.py.
If you need model parameters pretrained on cityscapes, you can download the pth file here with extraction code: 3i4g.