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Segmentation

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

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 benchmarks/mmsegmentation/mim_dist_train.sh ${CONFIG} ${PRETRAIN} ${GPUS}

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

Remarks:

  • CONFIG: Use config files under configs/benchmarks/mmsegmentation/ or write your own config files
  • PRETRAIN: the pre-trained model file (the backbone parameters only).
  • ${GPUS}: The number of GPUs that you want to use to train. We adopt 4 GPUs for segmentation tasks by default.

Example:

bash 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

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Test

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

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

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

Remarks:

  • ${CHECKPOINT}: The trained segmentation model that you want to test.

Example:

bash ./tools/benchmarks/mmsegmentation/mim_dist_test.sh \
configs/benchmarks/mmsegmentation/voc12aug/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py \
work_dir/segmentation_model.pth 4

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