S2ANet

Content

Introduction
Model Zoo
Getting Start
Deployment
Citations

Introduction

S2ANet is used to detect rotated objects.

Model Zoo

Model	Conv Type	mAP	Lr Scheduler	Angle	Aug	GPU Number	images/GPU	download	config
S2ANet	Conv	71.45	2x	le135	-	4	2	model	config
S2ANet	AlignConv	73.84	2x	le135	-	4	2	model	config

Notes:

if GPU number or mini-batch size is changed, learning rate should be adjusted according to the formula lr_new = lr_default * (batch_size_new * GPU_number_new) / (batch_size_default * GPU_number_default).
Models in model zoo is trained and tested with single scale by default. If MS is indicated in the data augmentation column, it means that multi-scale training and multi-scale testing are used. If RR is indicated in the data augmentation column, it means that RandomRotate data augmentation is used for training.
multiclass_nms is used here, which is slightly different from the original author's use of NMS.

Getting Start

Refer to Data-Preparation to prepare data.

1. Train

Single GPU Training

export CUDA_VISIBLE_DEVICES=0
python tools/train.py -c configs/rotate/s2anet/s2anet_1x_spine.yml

Multiple GPUs Training

export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/rotate/s2anet/s2anet_1x_spine.yml

You can use --evalto enable train-by-test.

2. Evaluation

python tools/eval.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=output/s2anet_1x_spine/model_final.pdparams

# Use a trained model to evaluate
python tools/eval.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=https://paddledet.bj.bcebos.com/models/s2anet_1x_spine.pdparams

3. Prediction

Executing the following command will save the image prediction results to the output folder.

python tools/infer.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=output/s2anet_1x_spine/model_final.pdparams --infer_img=demo/39006.jpg --draw_threshold=0.3

Prediction using models that provide training:

python tools/infer.py -c configs/rotate/s2anet/s2anet_1x_spine.yml -o weights=https://paddledet.bj.bcebos.com/models/s2anet_1x_spine.pdparams --infer_img=demo/39006.jpg --draw_threshold=0.3

4. DOTA Data evaluation

Execute the following command, will save each image prediction result in output folder txt text with the same folder name.

python tools/infer.py -c configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml -o weights=https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams --infer_dir=/path/to/test/images --output_dir=output --visualize=False --save_results=True

Refering to DOTA Task, You need to submit a zip file containing results for all test images for evaluation. The detection results of each category are stored in a txt file, each line of which is in the following format image_id score x1 y1 x2 y2 x3 y3 x4 y4. To evaluate, you should submit the generated zip file to the Task1 of DOTA Evaluation. You can execute the following command to generate the file

python configs/rotate/tools/generate_result.py --pred_txt_dir=output/ --output_dir=submit/ --data_type=dota10

zip -r submit.zip submit

Deployment

The inputs of the multiclass_nms operator in Paddle support quadrilateral inputs, so deployment can be done without relying on the rotating frame IOU operator.

Please refer to the deployment tutorialPredict deployment

Citations

@article{han2021align,  
  author={J. {Han} and J. {Ding} and J. {Li} and G. -S. {Xia}},  
  journal={IEEE Transactions on Geoscience and Remote Sensing},  
  title={Align Deep Features for Oriented Object Detection},  
  year={2021},
  pages={1-11},  
  doi={10.1109/TGRS.2021.3062048}}

@inproceedings{xia2018dota,
  title={DOTA: A large-scale dataset for object detection in aerial images},
  author={Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3974--3983},
  year={2018}
}

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