This paper has been accepted by IEEE Transactions on Circuits and Systems for Video Technology.
arXiv Link: https://arxiv.org/abs/2204.10704
Download SUES-200 dataset. Notices: SUES-200 is ONLY available to academic research.
Google Drive Link: https://drive.google.com/file/d/1UyVyFJ_pRaJHIr_eBY2HL7gkS5y9UxqI/view?usp=share_link
百度网盘： https://pan.baidu.com/s/1mrd-7ADm57_OchAvO1XmNw 提取码：p836
天翼网盘（不限速）： https://cloud.189.cn/t/yMnaEnR322Yj 提取码：veh7
My email : [email protected]
链接: https://pan.baidu.com/s/1aq51FLfg3bPG4xoNW1Usxw?pwd=rbnu 提取码: rbnu
- Install Pytorch Torchvision https://pytorch.org/get-started/locally/
- install other libs
pip install timm pyyaml pytorch-metric-learning scipy pandas opencv-python grad-cam einops imgaug
default: settings.yaml
# dateset path
dataset_path: /media/data1/Datasets
weight_save_path: /media/data1/save_model_weight
# intial parameters
fp16 : 0 # apex
classes : 120 # 200*0.6=120
image_size: 384
# choose model
model : resnet
# super parameters
batch_size : 32
num_epochs : 80
drop_rate : 0.2
weight_decay : 0.0005
lr : 0.005
# test and evaluate
# if LPN
block : 4
# if SUES-200
height : 150
python script/split_dataset.py --path your_path
mkdir your_path/Dataset
mv your_path/Training your_path/Dataset
mv your_path/Testing your_path/Datasetpython train.py --cfg settings.yamlpython test_and_evaluate.py --cfg settings.yaml --name resnet_150_2022-04-25-10:26:34 --seq 3python test_and_evaluate_uncertainties.py --cfg settings.yaml --types ["snow", "fog"] --heights [150, 200]python test_and_evaluate.py --dist Eu# Max Pooling
python multi_test_and_evaluate_pooling.py --type max
# Voting
python multi_test_and_evaluate_voting.py@ARTICLE{zhu2023sues,
author={Zhu, Runzhe and Yin, Ling and Yang, Mingze and Wu, Fei and Yang, Yuncheng and Hu, Wenbo},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
title={SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across Drone and Satellite},
year={2023},
volume={},
number={},
pages={1-1},
doi={10.1109/TCSVT.2023.3249204}}
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Improve README.md (ing...)
- Evaluation methods
- Visualization
- Multiqueries
- Draw heat map
- ...
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配置文件：settings.yaml
该配置文件配置了
- 数据集地址 dataset_path
- 权重文件保存地址 weight_save_path
- 选取不同高度的数据 height
- 训练时选用的特征提取模型 model
- 训练时的学习率 lr
- 训练轮数 num_epoch
- 模型中的drop out drop_rate
- 训练时的批次大小 batch_size
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开始训练：执行 train.py 会根据上面配置好的参数进行训练，比较好的模型权重会保存在权重文件保存地址下的save_model_weight文件夹中（训练时会自动创建该文件夹）
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开始测试：执行 test_and_evaluate.py 会开始测试并输出测试结果，最后的结果会保存在save_model_weight中
基于网格搜素的自动调参数文件：AutoTuning.py
定义特征提取算法的文件：model_.py
CBAM_ResNet 算法模型定义：senet/cbam_resnet.py
数据集预处理文件夹：script
VLAD 复现代码：VLAD文件夹
NetVLAD 复现代码：NetVLAD文件夹，train_NetVLAD.py test_NetVlAD.py
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