AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.

Overview

AdelaiDet

AdelaiDet is an open source toolbox for multiple instance-level recognition tasks on top of Detectron2. All instance-level recognition works from our group are open-sourced here.

To date, AdelaiDet implements the following algorithms:

Models

COCO Object Detecton Baselines with FCOS

Name inf. time box AP download
FCOS_R_50_1x 16 FPS 38.7 model
FCOS_MS_R_101_2x 12 FPS 43.1 model
FCOS_MS_X_101_32x8d_2x 6.6 FPS 43.9 model
FCOS_MS_X_101_32x8d_dcnv2_2x 4.6 FPS 46.6 model
FCOS_RT_MS_DLA_34_4x_shtw 52 FPS 39.1 model

More models can be found in FCOS README.md.

COCO Instance Segmentation Baselines with BlendMask

Model Name inf. time box AP mask AP download
Mask R-CNN R_101_3x 10 FPS 42.9 38.6
BlendMask R_101_3x 11 FPS 44.8 39.5 model
BlendMask R_101_dcni3_5x 10 FPS 46.8 41.1 model

For more models and information, please refer to BlendMask README.md.

COCO Instance Segmentation Baselines with MEInst

Name inf. time box AP mask AP download
MEInst_R_50_3x 12 FPS 43.6 34.5 model

For more models and information, please refer to MEInst README.md.

Total_Text results with ABCNet

Name inf. time e2e-hmean det-hmean download
v1-totaltext 11 FPS 67.1 86.0 model
v2-totaltext 7.7 FPS 71.8 87.2 model

For more models and information, please refer to ABCNet README.md.

COCO Instance Segmentation Baselines with CondInst

Name inf. time box AP mask AP download
CondInst_MS_R_50_1x 14 FPS 39.7 35.7 model
CondInst_MS_R_50_BiFPN_3x_sem 13 FPS 44.7 39.4 model
CondInst_MS_R_101_3x 11 FPS 43.3 38.6 model
CondInst_MS_R_101_BiFPN_3x_sem 10 FPS 45.7 40.2 model

For more models and information, please refer to CondInst README.md.

Note that:

  • Inference time for all projects is measured on a NVIDIA 1080Ti with batch size 1.
  • APs are evaluated on COCO2017 val split unless specified.

Installation

First install Detectron2 following the official guide: INSTALL.md.

Please use Detectron2 with commit id 9eb4831 if you have any issues related to Detectron2.

Then build AdelaiDet with:

git clone https://github.com/aim-uofa/AdelaiDet.git
cd AdelaiDet
python setup.py build develop

If you are using docker, a pre-built image can be pulled with:

docker pull tianzhi0549/adet:latest

Some projects may require special setup, please follow their own README.md in configs.

Quick Start

Inference with Pre-trained Models

  1. Pick a model and its config file, for example, fcos_R_50_1x.yaml.
  2. Download the model wget https://cloudstor.aarnet.edu.au/plus/s/glqFc13cCoEyHYy/download -O fcos_R_50_1x.pth
  3. Run the demo with
python demo/demo.py \
    --config-file configs/FCOS-Detection/R_50_1x.yaml \
    --input input1.jpg input2.jpg \
    --opts MODEL.WEIGHTS fcos_R_50_1x.pth

Train Your Own Models

To train a model with "train_net.py", first setup the corresponding datasets following datasets/README.md, then run:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/FCOS-Detection/R_50_1x.yaml \
    --num-gpus 8 \
    OUTPUT_DIR training_dir/fcos_R_50_1x

To evaluate the model after training, run:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/FCOS-Detection/R_50_1x.yaml \
    --eval-only \
    --num-gpus 8 \
    OUTPUT_DIR training_dir/fcos_R_50_1x \
    MODEL.WEIGHTS training_dir/fcos_R_50_1x/model_final.pth

Note that:

  • The configs are made for 8-GPU training. To train on another number of GPUs, change the --num-gpus.
  • If you want to measure the inference time, please change --num-gpus to 1.
  • We set OMP_NUM_THREADS=1 by default, which achieves the best speed on our machines, please change it as needed.
  • This quick start is made for FCOS. If you are using other projects, please check the projects' own README.md in configs.

Acknowledgements

The authors are grateful to Nvidia, Huawei Noah's Ark Lab, ByteDance, Adobe who generously donated GPU computing in the past a few years.

Citing AdelaiDet

If you use this toolbox in your research or wish to refer to the baseline results published here, please use the following BibTeX entries:

@misc{tian2019adelaidet,
  author =       {Tian, Zhi and Chen, Hao and Wang, Xinlong and Liu, Yuliang and Shen, Chunhua},
  title =        {{AdelaiDet}: A Toolbox for Instance-level Recognition Tasks},
  howpublished = {\url{https://git.io/adelaidet}},
  year =         {2019}
}

and relevant publications:

@inproceedings{tian2019fcos,
  title     =  {{FCOS}: Fully Convolutional One-Stage Object Detection},
  author    =  {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
  booktitle =  {Proc. Int. Conf. Computer Vision (ICCV)},
  year      =  {2019}
}

@article{tian2021fcos,
  title   =  {{FCOS}: A Simple and Strong Anchor-free Object Detector},
  author  =  {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
  journal =  {IEEE T. Pattern Analysis and Machine Intelligence (TPAMI)},
  year    =  {2021}
}

@inproceedings{chen2020blendmask,
  title     =  {{BlendMask}: Top-Down Meets Bottom-Up for Instance Segmentation},
  author    =  {Chen, Hao and Sun, Kunyang and Tian, Zhi and Shen, Chunhua and Huang, Yongming and Yan, Youliang},
  booktitle =  {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
  year      =  {2020}
}

@inproceedings{zhang2020MEInst,
  title     =  {Mask Encoding for Single Shot Instance Segmentation},
  author    =  {Zhang, Rufeng and Tian, Zhi and Shen, Chunhua and You, Mingyu and Yan, Youliang},
  booktitle =  {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
  year      =  {2020}
}

@inproceedings{liu2020abcnet,
  title     =  {{ABCNet}: Real-time Scene Text Spotting with Adaptive {B}ezier-Curve Network},
  author    =  {Liu, Yuliang and Chen, Hao and Shen, Chunhua and He, Tong and Jin, Lianwen and Wang, Liangwei},
  booktitle =  {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
  year      =  {2020}
}

@ARTICLE{9525302,
  author={Liu, Yuliang and Shen, Chunhua and Jin, Lianwen and He, Tong and Chen, Peng and Liu, Chongyu and Chen, Hao},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3107437}
}
  

@inproceedings{wang2020solo,
  title     =  {{SOLO}: Segmenting Objects by Locations},
  author    =  {Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei},
  booktitle =  {Proc. Eur. Conf. Computer Vision (ECCV)},
  year      =  {2020}
}

@inproceedings{wang2020solov2,
  title     =  {{SOLOv2}: Dynamic and Fast Instance Segmentation},
  author    =  {Wang, Xinlong and Zhang, Rufeng and Kong, Tao and Li, Lei and Shen, Chunhua},
  booktitle =  {Proc. Advances in Neural Information Processing Systems (NeurIPS)},
  year      =  {2020}
}

@article{wang2021solo,
  title   =  {{SOLO}: A Simple Framework for Instance Segmentation},
  author  =  {Wang, Xinlong and Zhang, Rufeng and Shen, Chunhua and Kong, Tao and Li, Lei},
  journal =  {IEEE T. Pattern Analysis and Machine Intelligence (TPAMI)},
  year    =  {2021}
}

@article{tian2019directpose,
  title   =  {{DirectPose}: Direct End-to-End Multi-Person Pose Estimation},
  author  =  {Tian, Zhi and Chen, Hao and Shen, Chunhua},
  journal =  {arXiv preprint arXiv:1911.07451},
  year    =  {2019}
}

@inproceedings{tian2020conditional,
  title     =  {Conditional Convolutions for Instance Segmentation},
  author    =  {Tian, Zhi and Shen, Chunhua and Chen, Hao},
  booktitle =  {Proc. Eur. Conf. Computer Vision (ECCV)},
  year      =  {2020}
}

@inproceedings{tian2021boxinst,
  title     =  {{BoxInst}: High-Performance Instance Segmentation with Box Annotations},
  author    =  {Tian, Zhi and Shen, Chunhua and Wang, Xinlong and Chen, Hao},
  booktitle =  {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
  year      =  {2021}
}

@inproceedings{wang2021densecl,
  title     =   {Dense Contrastive Learning for Self-Supervised Visual Pre-Training},
  author    =   {Wang, Xinlong and Zhang, Rufeng and Shen, Chunhua and Kong, Tao and Li, Lei},
  booktitle =   {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
  year      =   {2021}
}

@inproceedings{Mao2021pose,
  title     =   {{FCPose}: Fully Convolutional Multi-Person Pose Estimation With Dynamic Instance-Aware Convolutions},
  author    =   {Mao, Weian and  Tian, Zhi  and Wang, Xinlong  and Shen, Chunhua},
  booktitle =   {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
  year      =   {2021}
}

License

For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact Chunhua Shen.

Owner
Adelaide Intelligent Machines (AIM) Group
Adelaide Intelligent Machines (AIM) Group
Attention-guided gan for synthesizing IR images

SI-AGAN Attention-guided gan for synthesizing IR images This repository contains the Tensorflow code for "Pedestrian Gender Recognition by Style Trans

1 Oct 25, 2021
A PyTorch Implementation of "Neural Arithmetic Logic Units"

Neural Arithmetic Logic Units [WIP] This is a PyTorch implementation of Neural Arithmetic Logic Units by Andrew Trask, Felix Hill, Scott Reed, Jack Ra

Kevin Zakka 181 Nov 18, 2022
《LXMERT: Learning Cross-Modality Encoder Representations from Transformers》(EMNLP 2020)

The Most Important Thing. Our code is developed based on: LXMERT: Learning Cross-Modality Encoder Representations from Transformers

53 Dec 16, 2022
This is the implementation of the paper "Self-supervised Outdoor Scene Relighting"

Self-supervised Outdoor Scene Relighting This is the implementation of the paper "Self-supervised Outdoor Scene Relighting". The model is implemented

Ye Yu 24 Dec 17, 2022
Code for the paper "JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design"

JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design This repository contains code for the paper: JA

Aspuru-Guzik group repo 55 Nov 29, 2022
Implementation of "Learning to Match Features with Seeded Graph Matching Network" ICCV2021

SGMNet Implementation PyTorch implementation of SGMNet for ICCV'21 paper "Learning to Match Features with Seeded Graph Matching Network", by Hongkai C

87 Dec 11, 2022
The authors' official PyTorch SigWGAN implementation

The authors' official PyTorch SigWGAN implementation This repository is the official implementation of [Sig-Wasserstein GANs for Time Series Generatio

9 Jun 16, 2022
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Li

Jia Research Lab 137 Dec 14, 2022
Code for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in Deep Latent Space"

SRHEN This is a better and simpler implementation for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in

1 Oct 28, 2022
Official implementation of the paper "Topographic VAEs learn Equivariant Capsules"

Topographic Variational Autoencoder Paper: https://arxiv.org/abs/2109.01394 Getting Started Install requirements with Anaconda: conda env create -f en

T. Andy Keller 69 Dec 12, 2022
Source code of NeurIPS 2021 Paper ''Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration''

CaGCN This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration". Paper L

6 Dec 19, 2022
Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detection

Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detection abstract:Unlike 2D object detection where all RoI featur

DK. Zhang 2 Oct 07, 2022
Implementation of ICCV 2021 oral paper -- A Novel Self-Supervised Learning for Gaussian Mixture Model

SS-GMM Implementation of ICCV 2021 oral paper -- Self-Supervised Image Prior Learning with GMM from a Single Noisy Image with supplementary material R

HUST-The Tan Lab 4 Dec 05, 2022
Dataset and codebase for NeurIPS 2021 paper: Exploring Forensic Dental Identification with Deep Learning

Repository under construction. Example dataset, checkpoints, and training/testing scripts will be avaible soon! 💡 Collated best practices from most p

4 Jun 26, 2022
Apply AnimeGAN-v2 across frames of a video clip

title emoji colorFrom colorTo sdk app_file pinned AnimeGAN-v2 For Videos 🔥 blue red gradio app.py false AnimeGAN-v2 For Videos Apply AnimeGAN-v2 acro

Nathan Raw 36 Oct 18, 2022
TargetAllDomainObjects - A python wrapper to run a command on against all users/computers/DCs of a Windows Domain

TargetAllDomainObjects A python wrapper to run a command on against all users/co

Podalirius 19 Dec 13, 2022
Run Effective Large Batch Contrastive Learning on Limited Memory GPU

Gradient Cache Gradient Cache is a simple technique for unlimitedly scaling contrastive learning batch far beyond GPU memory constraint. This means tr

Luyu Gao 198 Dec 29, 2022
基于Pytorch实现优秀的自然图像分割框架!(包括FCN、U-Net和Deeplab)

语义分割学习实验-基于VOC数据集 usage: 下载VOC数据集,将JPEGImages SegmentationClass两个文件夹放入到data文件夹下。 终端切换到目标目录,运行python train.py -h查看训练 (torch) Li Xiang 28 Dec 21, 2022

PyTorch implementation for Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition.

Stochastic CSLR This is the PyTorch implementation for the ECCV 2020 paper: Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuou

Zhe Niu 28 Dec 19, 2022
Implementation of gaze tracking and demo

Predicting Customer Demand by Using Gaze Detecting and Object Tracking This project is the integration of gaze detecting and object tracking. Predict

2 Oct 20, 2022