TOOD: Task-aligned One-stage Object Detection, ICCV2021 Oral

Overview

TOOD: Task-aligned One-stage Object Detection (ICCV 2021 Oral)

Paper

Introduction

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks. In this work, we propose a Task-aligned One-stage Object Detection (TOOD) that explicitly aligns the two tasks in a learning-based manner. First, we design a novel Task-aligned Head (T-Head) which offers a better balance between learning task-interactive and task-specific features, as well as a greater flexibility to learn the alignment via a task-aligned predictor. Second, we propose Task Alignment Learning (TAL) to explicitly pull closer (or even unify) the optimal anchors for the two tasks during training via a designed sample assignment scheme and a task-aligned loss. Extensive experiments are conducted on MS-COCO, where TOOD achieves a 51.1 AP at single-model single-scale testing. This surpasses the recent one-stage detectors by a large margin, such as ATSS (47.7 AP), GFL (48.2 AP), and PAA (49.0 AP), with fewer parameters and FLOPs. Qualitative results also demonstrate the effectiveness of TOOD for better aligning the tasks of object classification and localization.

Method overview

Parallel head vs. T-head

method overview

Prerequisites

  • MMDetection version 2.14.0.

  • Please see get_started.md for installation and the basic usage of MMDetection.

Train

# assume that you are under the root directory of this project,
# and you have activated your virtual environment if needed.
# and with COCO dataset in 'data/coco/'.

./tools/dist_train.sh configs/tood/tood_r50_fpn_1x_coco.py 4

Inference

./tools/dist_test.sh configs/tood/tood_r50_fpn_1x_coco.py work_dirs/tood_r50_fpn_1x_coco/epoch_12.pth 4 --eval bbox

Models

For your convenience, we provide the following trained models (TOOD). All models are trained with 16 images in a mini-batch.

Model Anchor MS train DCN Lr schd AP (minival) AP (test-dev) Config Download
TOOD_R_50_FPN_1x Anchor-free No N 1x 42.5 42.7 config google / baidu
TOOD_R_50_FPN_anchor_based_1x Anchor-based No N 1x 42.4 42.8 config google / baidu
TOOD_R_101_FPN_2x Anchor-free Yes N 2x 46.2 46.7 config google / baidu
TOOD_X_101_FPN_2x Anchor-free Yes N 2x 47.6 48.5 config google / baidu
TOOD_R_101_dcnv2_FPN_2x Anchor-free Yes Y 2x 49.2 49.6 config google / baidu
TOOD_X_101_dcnv2_FPN_2x Anchor-free Yes Y 2x 50.5 51.1 config google / baidu

[0] All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..
[1] dcnv2 denotes deformable convolutional networks v2.
[2] Refer to more details in config files in config/tood/.
[3] Extraction code of baidu netdisk: tood.

Acknowledgement

Thanks MMDetection team for the wonderful open source project!

Citation

If you find TOOD useful in your research, please consider citing:

@inproceedings{feng2021tood,
    title={TOOD: Task-aligned One-stage Object Detection},
    author={Feng, Chengjian and Zhong, Yujie and Gao, Yu and Scott, Matthew R and Huang, Weilin},
    booktitle={ICCV},
    year={2021}
}
YOLO-v5 기반 단안 카메라의 영상을 활용해 차간 거리를 일정하게 유지하며 주행하는 Adaptive Cruise Control 기능 구현

자율 주행차의 영상 기반 차간거리 유지 개발 Table of Contents 프로젝트 소개 주요 기능 시스템 구조 디렉토리 구조 결과 실행 방법 참조 팀원 프로젝트 소개 YOLO-v5 기반으로 단안 카메라의 영상을 활용해 차간 거리를 일정하게 유지하며 주행하는 Adap

14 Jun 29, 2022
A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

AutoTrader AutoTrader is Python-based platform intended to help in the development, optimisation and deployment of automated trading systems. From sim

Kieran Mackle 485 Jan 09, 2023
Unofficial PyTorch Implementation for HifiFace (https://arxiv.org/abs/2106.09965)

HifiFace — Unofficial Pytorch Implementation Image source: HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping (figure 1, pg. 1)

MINDs Lab 218 Jan 04, 2023
Heterogeneous Temporal Graph Neural Network

Heterogeneous Temporal Graph Neural Network This repository contains the datasets and source code of HTGNN. run_mag.ipynb is the training and testing

15 Dec 22, 2022
[ICSE2020] MemLock: Memory Usage Guided Fuzzing

MemLock: Memory Usage Guided Fuzzing This repository provides the tool and the evaluation subjects for the paper "MemLock: Memory Usage Guided Fuzzing

Cheng Wen 54 Jan 07, 2023
Multi Camera Calibration

Multi Camera Calibration 'modules/camera_calibration/app/camera_calibration.cpp' is for calculating extrinsic parameter of each individual cameras. 'm

7 Dec 01, 2022
This implements one of result networks from Large-scale evolution of image classifiers

Exotic structured image classifier This implements one of result networks from Large-scale evolution of image classifiers by Esteban Real, et. al. Req

54 Nov 25, 2022
Implementation of "Deep Implicit Templates for 3D Shape Representation"

Deep Implicit Templates for 3D Shape Representation Zerong Zheng, Tao Yu, Qionghai Dai, Yebin Liu. arXiv 2020. This repository is an implementation fo

Zerong Zheng 144 Dec 07, 2022
Catch-all collection of generative art made using processing

Generative art with Processing.py Some art I have created for fun. Dependencies Processing for Python, see how to download/use here Packages contained

2 Mar 12, 2022
The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation(ICPR 2020) Overview This code is for the paper: Spatial Attention U-Net for Retinal V

Changlu Guo 151 Dec 28, 2022
This repo provides the base code for pytorch-lightning and weight and biases simultaneous integration.

Write your model faster with pytorch-lightning-wadb-code-backbone This repository provides the base code for pytorch-lightning and weight and biases s

9 Mar 29, 2022
Pytorch implementation of DeepMind's differentiable neural computer paper.

DNC pytorch This is a Pytorch implementation of DeepMind's Differentiable Neural Computer (DNC) architecture introduced in their recent Nature paper:

Yuanpu Xie 91 Nov 21, 2022
🐥A PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI

PyTorch implementation of OpenAI's Finetuned Transformer Language Model This is a PyTorch implementation of the TensorFlow code provided with OpenAI's

Hugging Face 1.4k Jan 05, 2023
QuadTree Attention for Vision Transformers (ICLR2022)

This repository contains codes for quadtree attention. This repo contains codes for feature matching, image classficiation, object detection and seman

tangshitao 222 Dec 28, 2022
An implementation of Deep Forest 2021.2.1.

Deep Forest (DF) 21 DF21 is an implementation of Deep Forest 2021.2.1. It is designed to have the following advantages: Powerful: Better accuracy than

LAMDA Group, Nanjing University 795 Jan 03, 2023
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
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022
Project page for End-to-end Recovery of Human Shape and Pose

End-to-end Recovery of Human Shape and Pose Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik CVPR 2018 Project Page Requirements Pyt

1.4k Dec 29, 2022
Funnels: Exact maximum likelihood with dimensionality reduction.

Funnels This repository contains the code needed to reproduce the experiments from the paper: Funnels: Exact maximum likelihood with dimensionality re

2 Apr 21, 2022
Reference implementation for Structured Prediction with Deep Value Networks

Deep Value Network (DVN) This code is a python reference implementation of DVNs introduced in Deep Value Networks Learn to Evaluate and Iteratively Re

Michael Gygli 55 Feb 02, 2022