Downstream tasks¶

Detection¶

For detection tasks, please use MMDetection. First, make sure you have installed MIM, which is also a project of OpenMMLab.

pip install openmim
mim install 'mmdet>=3.0.0rc0'

Besides, please refer to MMDet for installation and data preparation

Train¶

After installation, you can run MMDetection with simple command.

# distributed version
bash tools/benchmarks/mmdetection/mim_dist_train_c4.sh ${CONFIG} ${PRETRAIN} ${GPUS}
bash tools/benchmarks/mmdetection/mim_dist_train_fpn.sh ${CONFIG} ${PRETRAIN} ${GPUS}

# slurm version
bash tools/benchmarks/mmdetection/mim_slurm_train_c4.sh ${PARTITION} ${CONFIG} ${PRETRAIN}
bash tools/benchmarks/mmdetection/mim_slurm_train_fpn.sh ${PARTITION} ${CONFIG} ${PRETRAIN}

${CONFIG}: Use config file path in MMDetection directly. And for some algorithms, we also have some modified config files which can be found in the benchmarks folder under the correspondding algorithm folder. You can also writing your config file from scratch.
${PRETRAIN}: the pre-trained model file.
${GPUS}: The number of GPUs that you want to use to train. We adopt 8 GPUs for detection tasks by default.

Example:

bash ./tools/benchmarks/mmdetection/mim_dist_train_c4.sh \
  configs/byol/benchmarks/mask-rcnn_r50-c4_ms-1x_coco.py \
  https://download.openmmlab.com/mmselfsup/1.x/byol/byol_resnet50_16xb256-coslr-200e_in1k/byol_resnet50_16xb256-coslr-200e_in1k_20220825-de817331.pth 8

Test¶

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

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

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

${CONFIG}: Use config file name in MMDetection directly. And for some algorithms, we also have some modified config files which can be found in the benchmarks folder under the correspondding algorithm folder. You can also writing your config file from scratch.
${CHECKPOINT}: The fine-tuned detection model that you want to test.
${GPUS}: The number of GPUs that you want to use to test. We adopt 8 GPUs for detection tasks by default.

Example:

bash ./tools/benchmarks/mmdetection/mim_dist_test.sh \
configs/byol/benchmarks/mask-rcnn_r50_fpn_ms-1x_coco.py \
https://download.openmmlab.com/mmselfsup/1.x/byol/byol_resnet50_16xb256-coslr-200e_in1k/byol_resnet50_16xb256-coslr-200e_in1k_20220825-de817331.pth 8

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>=1.0.0rc0'

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

Train¶

After installation, you can run MMSegmentation 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}

${CONFIG}: Use config file path in MMSegmentation directly. And for some algorithms, we also have some modified config files which can be found in the benchmarks folder under the correspondding algorithm folder. You can also writing your config file from scratch.
${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}

${CONFIG}: Use config file name in MMSegmentation directly. And for some algorithms, we also have some modified config files which can be found in the benchmarks folder under the correspondding algorithm folder. You can also writing your config file from scratch.
${CHECKPOINT}: The fine-tuned segmentation model that you want to test.
${GPUS}: The number of GPUs that you want to use to test. We adopt 4 GPUs for segmentation tasks by default.

Example:

bash ./tools/benchmarks/mmsegmentation/mim_dist_test.sh  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