person_monitor_fy/dependence/PaddleDetection/configs/dota/README_en.md

S2ANet Model

Content

• S2ANet Model
  • Content
  • Introduction
  • Prepare Data
  • DOTA data
  • Customize Data
  • Start Training
  • 1. Install the rotating frame IOU and calculate the OP
  • 2. Train
  • 3. Evaluation
  • 4. Prediction
  • 5. DOTA Data evaluation
  • Model Library
  • S2ANet Model
  • Predict Deployment
  • Citations

Introduction

S2ANet is used to detect rotating frame's model, required use of PaddlePaddle 2.1.1(can be installed using PIP) or proper develop version.

Prepare Data

DOTA data

[DOTA Dataset] is a dataset of object detection in aerial images, which contains 2806 images with a resolution of 4000x4000 per image.

Data version	categories	images	size	instances	annotation method
v1.0	15	2806	800~4000	118282	OBB + HBB
v1.5	16	2806	800~4000	400000	OBB + HBB

Note: OBB annotation is an arbitrary quadrilateral; The vertices are arranged in clockwise order. The HBB annotation mode is the outer rectangle of the indicator note example.

There were 2,806 images in the DOTA dataset, including 1,411 images as a training set, 458 images as an evaluation set, and the remaining 937 images as a test set.

If you need to cut the image data, please refer to the DOTA_devkit.

After setting crop_size=1024, stride=824, gap=200 parameters to cut data, there are 15,749 images in the training set, 5,297 images in the evaluation set, and 10,833 images in the test set.

Customize Data

There are two ways to annotate data:

• The first is a tagging rotating rectangular, can pass rotating rectangular annotation tool roLabelImg to describe rotating rectangular box.

• The second is to mark the quadrilateral, through the script into an external rotating rectangle, so that the obtained mark may have a certain error with the real object frame.

Then convert the annotation result into coco annotation format, where each bbox is in the format of [x_center, y_center, width, height, angle], where the angle is expressed in radians.

Reference spinal disk dataset, we divide dataset into training set (230), the test set (57), data address is: spine_coco. The dataset has a small number of images, which can be used to train the S2ANet model quickly.

Start Training

1. Install the rotating frame IOU and calculate the OP

Rotate box IoU calculate ext_op is a reference PaddlePaddle custom external operator.

To use the rotating frame IOU to calculate the OP, the following conditions must be met:

• PaddlePaddle >= 2.1.1
• GCC == 8.2

Docker images are recommendedpaddle:2.1.1-gpu-cuda10.1-cudnn7。

Run the following command to download the image and start the container:

sudo nvidia-docker run -it --name paddle_s2anet -v $PWD:/paddle --network=host registry.baidubce.com/paddlepaddle/paddle:2.1.1-gpu-cuda10.1-cudnn7 /bin/bash

If the PaddlePaddle are installed in the mirror, go to python3.7 and run the following code to check whether the PaddlePaddle are installed properly:

import paddle
print(paddle.__version__)
paddle.utils.run_check()

enter ppdet/ext_op directory, install：

python3.7 setup.py install

In Windows, perform the following steps to install it：

（1）Visual Studio (version required >= Visual Studio 2015 Update3);

（2）Go to Start --> Visual Studio 2017 --> X64 native Tools command prompt for VS 2017;

（3）Setting Environment Variables：set DISTUTILS_USE_SDK=1

（4）Enter PaddleDetection/ppdet/ext_op directory，use python3.7 setup.py install to install。

After the installation, test whether the custom OP can compile normally and calculate the results:

cd PaddleDetecetion/ppdet/ext_op
python3.7 test.py

2. Train

Attention: In the configuration file, the learning rate is set based on the eight-card GPU training. If the single-card GPU training is used, set the learning rate to 1/8 of the original value.

Single GPU Training

export CUDA_VISIBLE_DEVICES=0
python3.7 tools/train.py -c configs/dota/s2anet_1x_spine.yml

Multiple GPUs Training

export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python3.7 -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/dota/s2anet_1x_spine.yml

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

3. Evaluation

python3.7 tools/eval.py -c configs/dota/s2anet_1x_spine.yml -o weights=output/s2anet_1x_spine/model_final.pdparams

# Use a trained model to evaluate
python3.7 tools/eval.py -c configs/dota/s2anet_1x_spine.yml -o weights=https://paddledet.bj.bcebos.com/models/s2anet_1x_spine.pdparams

Attention: (1) The DOTA dataset is trained together with train and val data as a training set, and the evaluation dataset configuration needs to be customized when evaluating the DOTA dataset.

(2) Bone dataset is transformed from segmented data. As there is little difference between different types of discs for detection tasks, and the score obtained by S2ANET algorithm is low, the default threshold for evaluation is 0.5, a low mAP is normal. You are advised to view the detection result visually.

4. Prediction

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

python3.7 tools/infer.py -c configs/dota/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:

python3.7 tools/infer.py -c configs/dota/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

5. DOTA Data evaluation

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

python3.7 tools/infer.py -c configs/dota/s2anet_alignconv_2x_dota.yml -o weights=./weights/s2anet_alignconv_2x_dota.pdparams  --infer_dir=dota_test_images --draw_threshold=0.05 --save_txt=True --output_dir=output

Please refer to DOTA_devkit generate assessment files, Assessment file format, please refer to DOTA Test, and generate the zip file, each class a txt file, every row in the txt file format for: image_id score x1 y1 x2 y2 x3 y3 x4 y4 You can also reference the dataset/dota_coco/dota_generate_test_result.py script to generate an evaluation file and submit it to the server.

Model Library

S2ANet Model

Model	Conv Type	mAP	Model Download	Configuration File
S2ANet	Conv	71.42	model	config
S2ANet	AlignConv	74.0	model	config

Attention: multiclass_nms is used here, which is slightly different from the original author's use of NMS.

Predict 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}
}