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
API for RL algorithm design & testing of BCA (Building Control Agent) HVAC on EnergyPlus building energy simulator by wrapping their EMS Python API

RL - EmsPy (work In Progress...) The EmsPy Python package was made to facilitate Reinforcement Learning (RL) algorithm research for developing and tes

20 Jan 05, 2023
🥈78th place in Riiid Solution🥈

Riiid Answer Correctness Prediction Introduction This repository is the code that placed 78th in Riiid Answer Correctness Prediction competition. Requ

ds wook 14 Apr 26, 2022
Official implementation of the paper ``Unifying Nonlocal Blocks for Neural Networks'' (ICCV'21)

Spectral Nonlocal Block Overview Official implementation of the paper: Unifying Nonlocal Blocks for Neural Networks (ICCV'21) Spectral View of Nonloca

91 Dec 14, 2022
A Runtime method overload decorator which should behave like a compiled language

strongtyping-pyoverload A Runtime method overload decorator which should behave like a compiled language there is a override decorator from typing whi

20 Oct 31, 2022
Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning

Manifold-SCA Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning The repo is org

Yuanyuan Yuan 172 Dec 29, 2022
PyTorch implementation of an end-to-end Handwritten Text Recognition (HTR) system based on attention encoder-decoder networks

AttentionHTR PyTorch implementation of an end-to-end Handwritten Text Recognition (HTR) system based on attention encoder-decoder networks. Scene Text

Dmitrijs Kass 31 Dec 22, 2022
competitions-v2

Codabench (formerly Codalab Competitions v2) Installation $ cp .env_sample .env $ docker-compose up -d $ docker-compose exec django ./manage.py migrat

CodaLab 21 Dec 02, 2022
Reproduces ResNet-V3 with pytorch

ResNeXt.pytorch Reproduces ResNet-V3 (Aggregated Residual Transformations for Deep Neural Networks) with pytorch. Tried on pytorch 1.6 Trains on Cifar

Pau Rodriguez 481 Dec 23, 2022
School of Artificial Intelligence at the Nanjing University (NJU)School of Artificial Intelligence at the Nanjing University (NJU)

F-Principle This is an exercise problem of the digital signal processing (DSP) course at School of Artificial Intelligence at the Nanjing University (

Thyrix 5 Nov 23, 2022
A robust pointcloud registration pipeline based on correlation.

PHASER: A Robust and Correspondence-Free Global Pointcloud Registration Ubuntu 18.04+ROS Melodic: Overview Pointcloud registration using correspondenc

ETHZ ASL 101 Dec 01, 2022
Sequence modeling benchmarks and temporal convolutional networks

Sequence Modeling Benchmarks and Temporal Convolutional Networks (TCN) This repository contains the experiments done in the work An Empirical Evaluati

CMU Locus Lab 3.5k Jan 01, 2023
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-t

Facebook Research 5.1k Jan 04, 2023
Capsule endoscopy detection DACON challenge

capsule_endoscopy_detection (DACON Challenge) Overview Yolov5, Yolor, mmdetection기반의 모델을 사용 (총 11개 모델 앙상블) 모든 모델은 학습 시 Pretrained Weight을 yolov5, yolo

MAILAB 11 Nov 25, 2022
Pytorch reimplementation of the Mixer (MLP-Mixer: An all-MLP Architecture for Vision)

MLP-Mixer Pytorch reimplementation of Google's repository for the MLP-Mixer (Not yet updated on the master branch) that was released with the paper ML

Eunkwang Jeon 18 Dec 08, 2022
Txt2Xml tool will help you convert from txt COCO format to VOC xml format in Object Detection Problem.

TXT 2 XML All codes assume running from root directory. Please update the sys path at the beginning of the codes before running. Over View Txt2Xml too

Nguyễn Trường Lâu 4 Nov 24, 2022
The Easy-to-use Dialogue Response Selection Toolkit for Researchers

Easy-to-use toolkit for retrieval-based Chatbot Recent Activity Our released RRS corpus can be found here. Our released BERT-FP post-training checkpoi

GMFTBY 32 Nov 13, 2022
Graph neural network message passing reframed as a Transformer with local attention

Adjacent Attention Network An implementation of a simple transformer that is equivalent to graph neural network where the message passing is done with

Phil Wang 49 Dec 28, 2022
Unrolled Generative Adversarial Networks

Unrolled Generative Adversarial Networks Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein arxiv:1611.02163 This repo contains an example notebo

Ben Poole 292 Dec 06, 2022
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation (CVPR'21)

EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation (CVPR'21) Citation If y

addisonwang 18 Nov 11, 2022
Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

LEXA Benchmark Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models

Oleg Rybkin 36 Dec 22, 2022