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Detection

MMDetection

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

It is very easy to install the package.

Besides, please refer to MMDet for installation and data preparation

Evaluation

After installing MMDet, you can run MMDetection with simple command. We provide scripts for the stage-4 only (C4) and FPN setting of object detection models.

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

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

Remarks:

  • CONFIG: Use config files under configs/benchmarks/mmdetection/ 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 8 GPUs for detection tasks by default.
  • 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'

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

Detectron2

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}

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Evaluation

After training, you can also run the command below with 8 GPUs (per node) to test your model.

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

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

Remarks:

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

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