A reimplementation of the S2ANet algorithm for Oriented Object Detection.
Note: Support DDP training and Auto Mixed Precision in Pytorch, so the training is faster!
We release the latest version !
- To normalize the inputing data, the images are divided by 255.0, instead of using
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], so we don't need to storage the mean and std in inference. - In training phase, we don't freeze parameters in the first stage of ResNet, which will add a little more training time.
- DDP training are more stable.
Our environment: Ubuntu18.04 + pytorch1.7 + cuda10.2
We don't try other environment, but we recommend pytorch>=1.6 for DDP training.
git clone https://github.com/chongkuiqi/S2ANet.git
cd S2ANet sudo apt-get install swig
cd DOTA_devkit/polyiou
swig -c++ -python csrc/polyiou.i
python setup.py build_ext --inplace cd S2ANet
python setup.py build_ext --inplaceWe take the DOTA dataset for example.
We should download the DOTA dataset, then change the folder structure like so:
your_dir
├── DOTA
│ ├── train
│ │ ├── images
│ │ ├── labelTxt
│ ├── val
│ │ ├── images
│ │ ├── labelTxt
The original images in DOTA have so large size, so we need to split them into chip images, like this:
note: the DOTA path and save path in 1_prepare_dota1_ms.py should be changed.
cd S2ANet/DOTA_devkit/
python 1_prepare_dota1_ms.pyThen convert the DOTA annotations to yolo format.
note: the DOTA_split path in 2_convert_dota_to_yolo.py should be changed.
python 2_convert_dota_to_yolo.pyBesides, val_split.txt is needed for evulate the model, this file records image names without extension.
note: the path in 3_create_txts.py should be changed.
python 3_create_txt.pyThe dota.yaml is also needed.
note: the path in dota.yaml should be changed.
Finally, the folder structure will be like this:
your_dir
├── DOTA
│ ├── train
│ │ ├── images
│ │ ├── labelTxt
│ ├── val
│ │ ├── images
│ │ ├── labelTxt
├── DOTA_split
│ ├── train
│ │ ├── empty_images
│ │ ├── empty_labels
│ │ ├── images
│ │ ├── labels
│ │ ├── labelTxt
│ ├── val
│ │ ├── images
│ │ ├── labels
│ │ ├── labelTxt
│ │ ├── val_split.txt
Most of the time, there is no need to modify the learning rate when setting different batch-size.
cd S2ANet
python train.pyNote: We support pytorch Multi-GPU DistributedDataParallel Mode !
python -m torch.distributed.launch --nproc_per_node 2 train.py --device 0,1 --batch-size 16If you get RuntimeError: Address already in use, use a different port number by adding --master_port like below,
python -m torch.distributed.launch --master_port 1234 --nproc_per_node 2 ...
Note: We only use the DOTA train set, and report the mAP50 on DOTA val set.
| Model | Backbone | train | mAP50 | Download |
|---|---|---|---|---|
| S2ANet (paper) | R-50-FPN | train+val set | 74.04(test set) | ------ |
| S2ANet (paper) | R-50-FPN | train set | 70.2(val set) | ------ |
| S2ANet (this impl.) | R-50-FPN | train set | 70.2(val set) | model |
| S2ANet (latest) | R-50-FPN | train set | 70.7(val set) | model |
- (1) Offical_S2ANet
- (2) YOLOv5
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