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42 lines
1.6 KiB
Markdown
42 lines
1.6 KiB
Markdown
# Detection
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- [Detection](#detection)
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- [Train](#train)
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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 MMDet 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.sh ${CONFIG} ${PRETRAIN} ${GPUS}
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# slurm version
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bash tools/benchmarks/mmdetection/mim_slurm_train.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/` or write your own config files
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- `PRETRAIN`: the pre-trained model file.
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Or if you want to do detection task with [detectron2](https://github.com/facebookresearch/detectron2), we also provides 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 benchmarks/detection
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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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