mmselfsup/docs/en/user_guides/detection.md

# Detection

- [Detection](#detection)
  - [Train](#train)
  - [Test](#test)

Here, we prefer to use MMDetection to do the detection task. First, make sure you have installed [MIM](https://github.com/open-mmlab/mim), which is also a project of OpenMMLab.

```shell
pip install openmim
```

It is very easy to install the package.

Besides, please refer to MMDet for [installation](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md) and [data preparation](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/1_exist_data_model.md)

## Train

After installation, you can run MMDetection with simple command.

```shell
# 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:

```shell
_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:

```shell
bash ./tools/benchmarks/mmdetection/mim_dist_train_c4.sh \
configs/benchmarks/mmdetection/coco/mask_rcnn_r50_c4_mstrain_1x_coco.py \
work_dir/byol_resnet50_8xb32-accum16-coslr-200e_in1k_20220225-5c8b2c2e.pth 8
```

Or if you want to do detection task with [detectron2](https://github.com/facebookresearch/detectron2), we also provide some config files.
Please refer to [INSTALL.md](https://github.com/facebookresearch/detectron2/blob/main/INSTALL.md) for installation and follow the [directory structure](https://github.com/facebookresearch/detectron2/tree/main/datasets) to prepare your datasets required by detectron2.

```shell
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.

```shell
# 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:

```shell
bash ./tools/benchmarks/mmdetection/mim_dist_test.sh \
configs/benchmarks/mmdetection/coco/mask_rcnn_r50_fpn_mstrain_1x_coco.py \
work_dir/epoch_12.pth 8
```