mmpretrain/docs/en/user_guides/downstream.md

# Downstream tasks

- [Downstream tasks](#downstream-tasks)
  - [Detection](#detection)
    - [Train](#train)
    - [Test](#test)
  - [Segmentation](#segmentation)
    - [Train](#train-1)
    - [Test](#test-1)

## Detection

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
mim install 'mmdet>=3.0.0rc0'
```

It is very easy to install the package.

Besides, please refer to MMDet for [installation](https://mmdetection.readthedocs.io/en/dev-3.x/get_started.html) and [data preparation](https://mmdetection.readthedocs.io/en/dev-3.x/user_guides/dataset_prepare.html)

### 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_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](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_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](https://github.com/open-mmlab/mim), which is also a project of OpenMMLab.

```shell
pip install openmim
mim install 'mmsegmentation>=1.0.0rc0'
```

It is very easy to install the package.

Besides, please refer to MMSegmentation for [installation](https://mmsegmentation.readthedocs.io/en/dev-1.x/get_started.html) and [data preparation](https://mmsegmentation.readthedocs.io/en/dev-1.x/user_guides/2_dataset_prepare.html).

### Train

After installation, you can run MMSeg with simple command.

```shell
# 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}
```

Remarks:

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

```shell
_base_ = 'mmseg::fcn/fcn_r50-d8_4xb2-40k_cityscapes-769x769.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 4 GPUs for segmentation tasks by default.

Example:

```shell
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.

```shell
# 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}
```

Remarks:

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

Example:

```shell
bash ./tools/benchmarks/mmsegmentation/mim_dist_test.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
```