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

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Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral)

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🏆 The 1st Place Submission to AICity Challenge 2021 Natural Language-Based Vehicle Retrieval Track (Alibaba-UTS submission)

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

The challenge for Quantum Coalition Hackathon 2021

People movement type classifier with YOLOv4 detection and SORT tracking.

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Simple ray intersection library similar to coldet - succedeed by libacc

Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection

Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

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

QAT(quantize aware training) for classification with MQBench

Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready inference.

Official implementation of "Refiner: Refining Self-attention for Vision Transformers".

Invariant Causal Prediction for Block MDPs

This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm.

This repository contains the code for "Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP".

Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems.

Repository for "Toward Practical Monocular Indoor Depth Estimation" (CVPR 2022)

MTCNN face detection implementation for TensorFlow, as a PIP package.

Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral)

AI-based, context-driven network device ranking

🏆 The 1st Place Submission to AICity Challenge 2021 Natural Language-Based Vehicle Retrieval Track (Alibaba-UTS submission)

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

The challenge for Quantum Coalition Hackathon 2021

People movement type classifier with YOLOv4 detection and SORT tracking.

Global-Local Attention for Emotion Recognition

Space Time Recurrent Memory Network - Pytorch

Simple ray intersection library similar to coldet - succedeed by libacc

Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection

Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.