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README.md
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This is the repo of **PointOBB-v2: Towards Simpler, Faster, and Stronger Single Point Supervised Oriented Object Detection**
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![pipeline]()
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### Installation
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Please refer to the [Installation](https://github.com/open-mmlab/mmrotate/blob/main/README.md), we copy it here.
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```
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conda create -n open-mmlab python=3.7 pytorch==1.7.0 cudatoolkit=10.1 torchvision -c pytorch -y
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conda activate open-mmlab
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pip install openmim
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mim install mmcv-full
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mim install mmdet
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git clone https://github.com/taugeren/PointOBB-v2.git
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cd mmrotate
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pip install -r requirements/build.txt
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pip install -v -e .
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```
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### Data Preparation
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Please follow [data_preparation](https://github.com/taugeren/PointOBB-v2/tree/main/tools/data) to prepare formatting data
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### Train CPM
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If you want to visualize CPM result during training, please set **visualize=True** in **train_config**
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Please modified the config code that contains the **visualize directory path**
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The learning rate for **n GPU card** and **batch size m** is 0.0125 * n * m
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For single GPU
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```
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# Basic format: python tools/train.py ${CONFIG_FILE} [optional arguments]
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python tools/train.py configs/pointobbv2/train_cpm_dotav10.py --work-dir work_dirs/cpm_dotav10 --gpu-ids 0
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```
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For multiple GPU
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```
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# Basic format: ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
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CUDA_VISIBLE_DEVICES=0,1 PORT=29801 ./tools/dist_train.sh configs/pointobbv2/train_cpm_dotav10.py 2
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```
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### Generate Pseudo Label
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Please modified the config code that contains the **directory path**
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For single GPU
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```
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# Basic fromat: python tools/train.py ${CONFIG_FILE} --resume-from ${CPM_CHECKPOINT_FILE} [other arguments]
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python tools/train.py configs/pointobbv2/generate_pseudo_label_dotav10.py --resume-from work_dirs/cpm_dotav10/epoch_6.pth --work-dir work_dirs/cpm_dotav10 --gpu-ids 0
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```
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For multiple GPU
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```
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# Basic format: ./tools/dist_train_resume.sh ${CONFIG_FILE} ${CPM_CHECKPOINT_FILE} ${GPU_NUM} [optional arguments]
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CUDA_VISIBLE_DEVICES=0,1 PORT=29801 ./tools/dist_train_resume.sh /ssd1/renbotao/github_submission/mmrotate/configs/pointobbv2/generate_pseudo_label_dotav10.py work_dirs/cpm_dotav10/epoch_6.pth 2
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```
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### Train Detector
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You can use different oriented object detection detector in [MMRotate Config](https://github.com/open-mmlab/mmrotate/tree/main/configs)
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Please modify the **pseudo label path** in config file
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For example, using Redet, the training command:
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```
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# single GPU
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python tools/train.py configs/pointobbv2/redet_dotav10.py --work-dir work_dirs/cpm_dotav10 --gpu-ids 0
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# multiple GPU
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CUDA_VISIBLE_DEVICES=0,1 PORT=29801 ./tools/dist_train.sh configs/pointobbv2/redet_dotav10.py 2
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```
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the testing command:
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```
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# single GPU
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python tools/test.py work_dirs/redet_dotav10/redet_dotav10.py work_dirs/redet_dotav10/epoch_12.pth --gpu-ids 0 --format-only --eval-options submission_dir=testmodel/redet_dotav10_epoch12
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# multiple GPU
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CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29816 tools/dist_test1.sh work_dirs/redet_dotav10/redet_dotav10.py work_dirs/redet_dotav10/epoch_12.pth 4
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```
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### Our log and checkpoint
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| Dataset | Config | Log | Checkpoint |
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| :-------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :---------------------------: |
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| DOTA-v1.0 | [config](https://github.com/taugeren/PointOBB-v2/tree/main/configs/pointobbv2) | [quark](https://pan.quark.cn/s/72c9fb568db8) [hugging face]() | [quark]() [hugging face]() |
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| DOTA-v1.5 | [config](https://github.com/taugeren/PointOBB-v2/tree/main/configs/pointobbv2) | [quark]() [hugging face]() | [quark]() [hugging face]() |
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| DOTA-v2.0 | [config](https://github.com/taugeren/PointOBB-v2/tree/main/configs/pointobbv2) | [quark]() [hugging face]() | [quark]() [hugging face]() |
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