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

Related tags

Deep Learningocrsolotext-recognitionobject-detectiontext-detectioninstance-segmentationfcosabcnetadelaidetblendmaskmeinstsolov2condinstboxinstdensecl
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
GitHub Repository https://git.io/AdelaiDet
ICCV2021 Oral SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks

Sign-Agnostic Convolutional Occupancy Networks Paper | Supplementary | Video | Teaser Video | Project Page This repository contains the implementation

63 Nov 18, 2022
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image

Learning to Reconstruct 3D Manhattan Wireframes From a Single Image This repository contains the PyTorch implementation of the paper: Yichao Zhou, Hao

Yichao Zhou 50 Dec 27, 2022
Code for paper "Multi-level Disentanglement Graph Neural Network"

Multi-level Disentanglement Graph Neural Network (MD-GNN) This is a PyTorch implementation of the MD-GNN, and the code includes the following modules:

Lirong Wu 6 Dec 29, 2022
This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard e

Soubhik Sanyal 71 Dec 30, 2022
Code for the paper "Implicit Representations of Meaning in Neural Language Models"

Implicit Representations of Meaning in Neural Language Models Preliminaries Create and set up a conda environment as follows: conda create -n state-pr

Belinda Li 39 Nov 03, 2022
Deep Learning for 3D Point Clouds: A Survey (IEEE TPAMI, 2020)

🔥Deep Learning for 3D Point Clouds (IEEE TPAMI, 2020)

Qingyong 1.4k Jan 08, 2023
Face Alignment using python

Face Alignment Face Alignment using python Input Image Aligned Face Aligned Face Aligned Face Input Image Aligned Face Input Image Aligned Face Instal

Sajjad Aemmi 28 Nov 23, 2022
Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

LUNAR Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks" Adam Goodge, Bryan Hooi, Ng See Kiong and

Adam Goodge 25 Dec 28, 2022
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation

LAVT: Language-Aware Vision Transformer for Referring Image Segmentation Where we are ? 12.27 目前和原论文仍有1%左右得差距，但已经力压很多SOTA了 ckpt__448_epoch_25.pth mIoU

zichengsaber 60 Dec 11, 2022
Implements Gradient Centralization and allows it to use as a Python package in TensorFlow

Gradient Centralization TensorFlow This Python package implements Gradient Centralization in TensorFlow, a simple and effective optimization technique

Rishit Dagli 101 Nov 01, 2022
A 3D Dense mapping backend library of SLAM based on taichi-Lang designed for the aerial swarm.

TaichiSLAM This project is a 3D Dense mapping backend library of SLAM based Taichi-Lang, designed for the aerial swarm. Intro Taichi is an efficient d

XuHao 230 Dec 19, 2022
Pytorch Performace Tuning, WandB, AMP, Multi-GPU, TensorRT, Triton

Plant Pathology 2020 FGVC7 Introduction A deep learning model pipeline for training, experimentaiton and deployment for the Kaggle Competition, Plant

Bharat Giddwani 0 Feb 25, 2022
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021)

Code for HDR Video Reconstruction HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021) Guanying Chen, Cha

Guanying Chen 64 Nov 19, 2022
Code for CVPR 2021 paper: Anchor-Free Person Search

Introduction This is the implementationn for Anchor-Free Person Search in CVPR2021 License This project is released under the Apache 2.0 license. Inst

158 Jan 04, 2023
An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come

IceVision is the first agnostic computer vision framework to offer a curated collection with hundreds of high-quality pre-trained models from torchvision, MMLabs, and soon Pytorch Image Models. It or

airctic 789 Dec 29, 2022
Pgn2tex - Scripts to convert pgn files to latex document. Useful to build books or pdf from pgn studies

Pgn2Latex (WIP) A simple script to make pdf from pgn files and studies. It's sti

12 Jul 23, 2022
Memory efficient transducer loss computation

Introduction This project implements the optimization techniques proposed in Improving RNN Transducer Modeling for End-to-End Speech Recognition to re

Fangjun Kuang 51 Nov 25, 2022
Code repository for "Free View Synthesis", ECCV 2020.

Free View Synthesis Code repository for "Free View Synthesis", ECCV 2020. Setup Install the following Python packages in your Python environment - num

Intelligent Systems Lab Org 253 Dec 07, 2022
Python Jupyter kernel using Poetry for reproducible notebooks

Poetry Kernel Use per-directory Poetry environments to run Jupyter kernels. No need to install a Jupyter kernel per Python virtual environment! The id

Pathbird 204 Jan 04, 2023
Released code for Objects are Different: Flexible Monocular 3D Object Detection, CVPR21

MonoFlex Released code for Objects are Different: Flexible Monocular 3D Object Detection, CVPR21. Work in progress. Installation This repo is tested w

Yunpeng 169 Dec 06, 2022