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paddle_detection/configs/rotate/s2anet/README_en.md

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+English | [简体中文](README.md)
+
+# S2ANet
+
+## Content
+- [Introduction](#Introduction)
+- [Model Zoo](#Model-Zoo)
+- [Getting Start](#Getting-Start)
+- [Deployment](#Deployment)
+- [Citations](#Citations)
+
+## Introduction
+
+[S2ANet](https://arxiv.org/pdf/2008.09397.pdf) 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](https://paddledet.bj.bcebos.com/models/s2anet_conv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_conv_2x_dota.yml) |
+| S2ANet | AlignConv | 73.84 | 2x | le135 | - | 4 | 2 | [model](https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/s2anet/s2anet_alignconv_2x_dota.yml) |
+
+**Notes:**
+- if **GPU number** or **mini-batch size** is changed, **learning rate** should be adjusted according to the formula **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**.
+- 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](../README_en.md#Data-Preparation) to prepare data.
+
+### 1. Train
+
+Single GPU Training
+```bash
+export CUDA_VISIBLE_DEVICES=0
+python tools/train.py -c configs/rotate/s2anet/s2anet_1x_spine.yml
+```
+
+Multiple GPUs Training
+```bash
+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 `--eval`to enable train-by-test.
+
+### 2. Evaluation
+```bash
+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.
+```bash
+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:
+```bash
+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](https://captain-whu.github.io/DOTA/tasks.html), 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](https://captain-whu.github.io/DOTA/evaluation.html). 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 tutorial[Predict deployment](../../../deploy/README_en.md)
+
+
+## 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}
+}
+```