Detection¶

Detection
- Train
- Test

Here, we prefer to use MMDetection to do the detection task. First, make sure you have installed MIM, which is also a project of OpenMMLab.

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

It is very easy to install the package.

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}

Remarks:

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

_base_ = 'mmdet::mask_rcnn/mask-rcnn_r50-caffe-c4_1x_coco.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 8 GPUs for detection tasks by default.

Example:

bash ./tools/benchmarks/mmdetection/mim_dist_train_c4.sh \
configs/benchmarks/mmdetection/coco/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

Or if you want to do detection task with detectron2, we also provide some config files. Please refer to INSTALL.md for installation and follow the directory structure to prepare your datasets required by detectron2.

conda activate detectron2 # use detectron2 environment here, otherwise use open-mmlab environment
cd tools/benchmarks/detectron2
python convert-pretrain-to-detectron2.py ${WEIGHT_FILE} ${OUTPUT_FILE} # must use .pkl as the output extension.
bash run.sh ${DET_CFG} ${OUTPUT_FILE}

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}

Remarks:

${CHECKPOINT}: The well-trained detection model that you want to test.

Example:

bash ./tools/benchmarks/mmdetection/mim_dist_test.sh \
configs/benchmarks/mmdetection/coco/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