This repo is customed for VisDrone.

Related tags

Deep Learningtensorflowobject-detectionfpncascade-rcnnvisdronemodel-ensemble
Overview

Object Detection for VisDrone(无人机航拍图像目标检测)

My environment

1、Windows10 (Linux available)
2、tensorflow >= 1.12.0
3、python3.6 (anaconda)
4、cv2
5、ensemble-boxes(pip install ensemble-boxes)

Datasets(XML format for training set)

(1).Datasets is available on https://github.com/VisDrone/VisDrone-Dataset
(2).Please download xml annotations on Baidu Yun (提取码: ia3f), or Google Drive, and configure it in ./core/config/cfgs.py
(3).You can also use ./data/visdrone2xml.py to generate your visdrone xml files, modify the path information.

training-set format:

├── VisDrone2019-DET-train
│     ├── Annotation(xml format)
│     ├── JPEGImages

Pretrained Models(ResNet50vd, 101vd)

Please download pretrained models on Baidu Yun (提取码: krce), or Google Drive, then put it into ./data/pretrained_weights

Train

Modify the parameters in ./core/config/cfgs.py
python train_step.py

Eval

Modify the parameters in ./core/config/cfgs.py
python eval_visdrone.py, it will get txt format file, then use official matlab tools to eval the final results.
python eval_model_ensemble.py. Before the running of this file, you should set NORMALIZED_RESULTS_FOR_MODEL_ENSEMBLE=True in cfgs.py and then run eval_visdrone.py to get normalized txt result.

Visualization

Modify the parameters in ./core/config/cfgs.py
python image_demo.py, it will get visualized results.

Visualized Result (multi-scale training+multi-scale testing) 1

Test Result(Validation set)：

1. ResNet50-vd

Name maxDets Result(s/m)
Average Precision (AP) @( IoU=0.50:0.95) maxDets=500 31.26%/35.1%
Average Precision (AP) @( IoU=0.50 ) maxDets=500 56.44%/60.29%
Average Precision (AP) @( IoU=0.75 ) maxDets=500 30.13%/35.42%
Average Recall (AR) @( IoU=0.50:0.95) maxDets= 1 0.78%/0.58%
Average Recall (AR) @( IoU=0.50:0.95) maxDets= 10 6.62%/6.05%
Average Recall (AR) @( IoU=0.50:0.95) maxDets=100 38.21%/40.99%
Average Recall (AR) @( IoU=0.50:0.95) maxDets=500 48.41%/53%
"s" means single-scale training + single-scale testing; "m"means multi-scale training + multi-scale testing

2. ResNet101-vd

Name maxDets Result(s/m)
Average Precision (AP) @( IoU=0.50:0.95) maxDets=500 31.7%/35.98%
Average Precision (AP) @( IoU=0.50 ) maxDets=500 56.94%/61.64%
Average Precision (AP) @( IoU=0.75 ) maxDets=500 30.59%/36.13%
Average Recall (AR) @( IoU=0.50:0.95) maxDets= 1 0.67%/0.61%
Average Recall (AR) @( IoU=0.50:0.95) maxDets= 10 6.29%/6.13%
Average Recall (AR) @( IoU=0.50:0.95) maxDets=100 38.66%/42.33%
Average Recall (AR) @( IoU=0.50:0.95) maxDets=500 49.29%/53.68%

3. Model Ensemble (ResNet101-vd+ResNet50-vd)

Name maxDets Result
Average Precision (AP) @( IoU=0.50:0.95) maxDets=500 36.76%
Average Precision (AP) @( IoU=0.50 ) maxDets=500 62.33%
Average Precision (AP) @( IoU=0.75 ) maxDets=500 37.41%
Average Recall (AR) @( IoU=0.50:0.95) maxDets= 1 0.59%
Average Recall (AR) @( IoU=0.50:0.95) maxDets= 10 6.06%
Average Recall (AR) @( IoU=0.50:0.95) maxDets=100 42.57%
Average Recall (AR) @( IoU=0.50:0.95) maxDets=500 54.53%
You can download trained weights(ResNet50vd, 101vd) on Baidu Yun (提取码: 9u9m), or Google Drive, then put it into ./saved_weights

Reference

1、https://github.com/DetectionTeamUCAS/Faster-RCNN_Tensorflow
2、https://github.com/open-mmlab/mmdetection
3、https://github.com/ZFTurbo/Weighted-Boxes-Fusion
4、https://github.com/kobiso/CBAM-tensorflow-slim
5、https://github.com/SJTU-Thinklab-Det/DOTA-DOAI
6、https://github.com/Viredery/tf-eager-fasterrcnn
7、https://github.com/VisDrone/VisDrone2018-DET-toolkit
8、https://github.com/YunYang1994/tensorflow-yolov3
9、https://github.com/zhpmatrix/VisDrone2018

Owner
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