This repo is customed for VisDrone.

Last update: Jul 17, 2022

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)

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

This repo is customed for VisDrone.

Related tags

Overview

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

My environment

Datasets(XML format for training set)

Pretrained Models(ResNet50vd, 101vd)

Train

Eval

Visualization

Test Result(Validation set)：

1. ResNet50-vd

"s" means single-scale training + single-scale testing; "m"means multi-scale training + multi-scale testing

2. ResNet101-vd

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

You can download trained weights(ResNet50vd, 101vd) on Baidu Yun (提取码: 9u9m), or Google Drive, then put it into ./saved_weights

Reference

Owner

PyTorch implementation of DeepDream algorithm

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Graph neural network message passing reframed as a Transformer with local attention

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

Official implementation of "Open-set Label Noise Can Improve Robustness Against Inherent Label Noise" (NeurIPS 2021)

Focal and Global Knowledge Distillation for Detectors

(NeurIPS 2020) Wasserstein Distances for Stereo Disparity Estimation

The Official PyTorch Implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 spotlight paper)

Recreate CenternetV2 based on MMDET.

This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning].

MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

This is an implementation for the CVPR2020 paper "Learning Invariant Representation for Unsupervised Image Restoration"

Alphabetical Letter Recognition

Robustness between the worst and average case

Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data

Python binding for Khiva library.

A crash course in six episodes for software developers who want to become machine learning practitioners.

Leveraging OpenAI's Codex to solve cornerstone problems in Music

Deep Learning to Create StepMania SM FIles

Official code repository for ICCV 2021 paper: Gravity-Aware Monocular 3D Human Object Reconstruction