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Segmentation

Segmentation
- Train
- Test

For semantic segmentation task, we use MMSegmentation. First, make sure you have installed MIM, which is also a project of OpenMMLab.

pip install openmim
mim install 'mmsegmentation>=1.0.0rc0'

It is very easy to install the package.

Besides, please refer to MMSegmentation for installation and data preparation.

Train

After installation, you can run MMSeg with simple command.

# distributed version
bash tools/benchmarks/mmsegmentation/mim_dist_train.sh ${CONFIG} ${PRETRAIN} ${GPUS}

# slurm version
bash tools/benchmarks/mmsegmentation/mim_slurm_train.sh ${PARTITION} ${CONFIG} ${PRETRAIN}

Remarks:

${CONFIG}: Use config files under configs/benchmarks/mmsegmentation/. Since repositories of OpenMMLab have support referring config files across different repositories, we can easily leverage the configs from MMSegmentation like:

_base_ = 'mmseg::fcn/fcn_r50-d8_4xb2-40k_cityscapes-769x769.py'

Writing your config files from scratch is also supported.

${PRETRAIN}: the pre-trained model file.
${GPUS}: The number of GPUs that you want to use to train. We adopt 4 GPUs for segmentation tasks by default.

Example:

bash ./tools/benchmarks/mmsegmentation/mim_dist_train.sh \
configs/benchmarks/mmsegmentation/voc12aug/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py \
https://download.openmmlab.com/mmselfsup/1.x/byol/byol_resnet50_16xb256-coslr-200e_in1k/byol_resnet50_16xb256-coslr-200e_in1k_20220825-de817331.pth 4

Test

After training, you can also run the command below to test your model.

# distributed version
bash tools/benchmarks/mmsegmentation/mim_dist_test.sh ${CONFIG} ${CHECKPOINT} ${GPUS}

# slurm version
bash tools/benchmarks/mmsegmentation/mim_slurm_test.sh ${PARTITION} ${CONFIG} ${CHECKPOINT}