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

Joint Versus Independent Multiview Hashing for Cross-View Retrieval[J] (IEEE TCYB 2021, PyTorch Code)

Revisiting Self-Training for Few-Shot Learning of Language Model.

Solution to the first stage Quiz of Hamoye internship: Introduction to Python for Machine Learning

Real-world Anomaly Detection in Surveillance Videos- pytorch Re-implementation

Clustergram - Visualization and diagnostics for cluster analysis in Python

Official implementation of deep-multi-trajectory-based single object tracking (IEEE T-CSVT 2021).

Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

This package implements THOR: Transformer with Stochastic Experts.

ncnn is a high-performance neural network inference framework optimized for the mobile platform

A minimal implementation of Gaussian process regression in PyTorch

A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm

Simple image captioning model - CLIP prefix captioning.

Automatic library of congress classification, using word embeddings from book titles and synopses.

PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

Hcaptcha-challenger - Gracefully face hCaptcha challenge with Yolov5(ONNX) embedded solution

A clear, concise, simple yet powerful and efficient API for deep learning.

This is an implementation of Googles Yogi-Optimizer in Keras (tf.keras)

Multi-task Multi-agent Soft Actor Critic for SMAC

Unofficial PyTorch implementation of Fastformer based on paper "Fastformer: Additive Attention Can Be All You Need"."

MoCoPnet - Deformable 3D Convolution for Video Super-Resolution