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

Source code for TACL paper "KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation".

[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)

This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

PyTorch implementation of neural style randomization for data augmentation

Rethinking the U-Net architecture for multimodal biomedical image segmentation

A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN)

Best Practices on Recommendation Systems

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Quantum-enhanced transformer neural network

This repository contains the source code of an efficient 1D probabilistic model for music time analysis proposed in ICASSP2022 venue.

We provided a matlab implementation for an evolutionary multitasking AUC optimization framework (EMTAUC).

This repository is the official implementation of Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models

EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration

A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Answer a series of contextually-dependent questions like they may occur in natural human-to-human conversations.

Synthetic Humans for Action Recognition, IJCV 2021

StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking

A Game-Theoretic Perspective on Risk-Sensitive Reinforcement Learning

Official implementation of the ICCV 2021 paper "Joint Inductive and Transductive Learning for Video Object Segmentation"