86 lines
3.0 KiB
Markdown
86 lines
3.0 KiB
Markdown
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# Detection
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- [Detection](#detection)
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- [Train](#train)
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- [Test](#test)
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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.
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```shell
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pip install openmim
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```
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It is very easy to install the package.
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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)
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## Train
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After installation, you can run MMDetection with simple command.
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```shell
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# distributed version
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bash tools/benchmarks/mmdetection/mim_dist_train_c4.sh ${CONFIG} ${PRETRAIN} ${GPUS}
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bash tools/benchmarks/mmdetection/mim_dist_train_fpn.sh ${CONFIG} ${PRETRAIN} ${GPUS}
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# slurm version
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bash tools/benchmarks/mmdetection/mim_slurm_train_c4.sh ${PARTITION} ${CONFIG} ${PRETRAIN}
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bash tools/benchmarks/mmdetection/mim_slurm_train_fpn.sh ${PARTITION} ${CONFIG} ${PRETRAIN}
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```
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Remarks:
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- `CONFIG`: Use config files under `configs/benchmarks/mmdetection/`. Since repositories of OpenMMLab have support referring config files across different
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repositories, we can easily leverage the configs from MMDetection like:
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```shell
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_base_ = 'mmdet::mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py'
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```
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Writing your config files from scratch is also supported.
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- `PRETRAIN`: the pre-trained model file.
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- `GPUS`: The number of GPUs that you want to use to train. We adopt 8 GPUs for detection tasks by default.
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Example:
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```shell
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bash ./tools/benchmarks/mmdetection/mim_dist_train_c4.sh \
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configs/benchmarks/mmdetection/coco/mask_rcnn_r50_c4_mstrain_1x_coco.py \
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work_dir/byol_resnet50_8xb32-accum16-coslr-200e_in1k_20220225-5c8b2c2e.pth 8
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```
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Or if you want to do detection task with [detectron2](https://github.com/facebookresearch/detectron2), we also provide some config files.
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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.
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```shell
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conda activate detectron2 # use detectron2 environment here, otherwise use open-mmlab environment
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cd tools/benchmarks/detectron2
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python convert-pretrain-to-detectron2.py ${WEIGHT_FILE} ${OUTPUT_FILE} # must use .pkl as the output extension.
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bash run.sh ${DET_CFG} ${OUTPUT_FILE}
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```
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## Test
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After training, you can also run the command below to test your model.
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```shell
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# distributed version
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bash tools/benchmarks/mmdetection/mim_dist_test.sh ${CONFIG} ${CHECKPOINT} ${GPUS}
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# slurm version
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bash tools/benchmarks/mmdetection/mim_slurm_test.sh ${PARTITION} ${CONFIG} ${CHECKPOINT}
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```
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Remarks:
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- `CHECKPOINT`: The well-trained detection model that you want to test.
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Example:
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```shell
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bash ./tools/benchmarks/mmdetection/mim_dist_test.sh \
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configs/benchmarks/mmdetection/coco/mask_rcnn_r50_fpn_mstrain_1x_coco.py \
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work_dir/epoch_12.pth 8
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```
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