Dynamic Head: Unifying Object Detection Heads with Attentions

Overview

Dynamic Head: Unifying Object Detection Heads with Attentions

PWC PWC

dyhead_video.mp4

This is the official implementation of CVPR 2021 paper "Dynamic Head: Unifying Object Detection Heads with Attentions".

"In this paper, we present a novel dynamic head framework to unify object detection heads with attentions. By coherently combining multiple self-attention mechanisms between feature levels for scale-awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness, the proposed approach significantly improves the representation ability of object detection heads without any computational overhead."

Dynamic Head: Unifying Object Detection Heads With Attentions

Xiyang Dai, Yinpeng Chen, Bin Xiao, Dongdong Chen, Mengchen Liu, Lu Yuan, Lei Zhang

Model Zoo

Code and Model are under internal review and will release soon. Stay tuned!

In order to open-source, we have ported the implementation from our internal framework to Detectron2 and re-train the models.

We notice better performances on some models compared to original paper.

Config Model Backbone Scheduler COCO mAP Weight
cfg FasterRCNN + DyHead R50 1x 40.3 weight
cfg RetinaNet + DyHead R50 1x 39.9 weight
cfg ATSS + DyHead R50 1x 42.4 weight
cfg ATSS + DyHead Swin-Tiny 2x + ms 49.8 weight

Usage

Dependencies:

Detectron2, timm

Installation:

python -m pip install -e DynamicHead

Train:

To train a config on a single node with 8 gpus, simply use:

DETECTRON2_DATASETS=$DATASET python train_net.py --config configs/dyhead_r50_retina_fpn_1x.yaml --num-gpus 8

Test:

To test a config with a weight on a single node with 8 gpus, simply use:

DETECTRON2_DATASETS=$DATASET python train_net.py --config configs/dyhead_r50_retina_fpn_1x.yaml --num-gpus 8 --eval-only MODEL.WEIGHTS $WEIGHT

Citation

@InProceedings{Dai_2021_CVPR,
    author    = {Dai, Xiyang and Chen, Yinpeng and Xiao, Bin and Chen, Dongdong and Liu, Mengchen and Yuan, Lu and Zhang, Lei},
    title     = {Dynamic Head: Unifying Object Detection Heads With Attentions},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {7373-7382}
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
Expand human face editing via Global Direction of StyleCLIP, especially to maintain similarity during editing.

Oh-My-Face This project is based on StyleCLIP, RIFE, and encoder4editing, which aims to expand human face editing via Global Direction of StyleCLIP, e

AiLin Huang 51 Nov 17, 2022
paper: Hyperspectral Remote Sensing Image Classification Using Deep Convolutional Capsule Network

DC-CapsNet This is a tensorflow and keras based implementation of DC-CapsNet for HSI in the Remote Sensing Letters R. Lei et al., "Hyperspectral Remot

LEI 7 Nov 29, 2022
Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows Official implementation of the paper DeFlow: Learning Complex Im

Valentin Wolf 86 Nov 16, 2022
Code for ACL2021 long paper: Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases

LANKA This is the source code for paper: Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (ACL 2021, long paper) Referen

Boxi Cao 30 Oct 24, 2022
Code implementation from my Medium blog post: [Transformers from Scratch in PyTorch]

transformer-from-scratch Code for my Medium blog post: Transformers from Scratch in PyTorch Note: This Transformer code does not include masked attent

Frank Odom 27 Dec 21, 2022
PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

halo 368 Dec 06, 2022
Official implementation of "Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform", ICCV 2021

Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform This repository is the implementation of "Variable-Rate Deep Image C

Myungseo Song 47 Dec 13, 2022
AFLFast (extends AFL with Power Schedules)

AFLFast Power schedules implemented by Marcel Böhme [email protected]

Marcel Böhme 380 Jan 03, 2023
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)

Maximum Likelihood Training of Score-Based Diffusion Models This repo contains the official implementation for the paper Maximum Likelihood Training o

Yang Song 84 Dec 12, 2022
PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML)

pytorch-maml This is a PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML): https://arxiv

Kate Rakelly 516 Jan 05, 2023
My implementation of Image Inpainting - A deep learning Inpainting model

Image Inpainting What is Image Inpainting Image inpainting is a restorative process that allows for the fixing or removal of unwanted parts within ima

Joshua V Evans 1 Dec 12, 2021
Implementation of "Large Steps in Inverse Rendering of Geometry"

Large Steps in Inverse Rendering of Geometry ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia), December 2021. Baptiste Nicolet · Alec Jacob

RGL: Realistic Graphics Lab 274 Jan 06, 2023
BoxInst: High-Performance Instance Segmentation with Box Annotations

Introduction This repository is the code that needs to be submitted for OpenMMLab Algorithm Ecological Challenge, the paper is BoxInst: High-Performan

88 Dec 21, 2022
Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning

Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning Reference Abeßer, J. & Müller, M. Towards Audio Domain Adapt

Jakob Abeßer 2 Jul 06, 2022
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

FusionNet_Pytorch FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics Requirements Pytorch 0.1.11 Pyt

Choi Gunho 102 Dec 13, 2022
[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

PG-MORL This repository contains the implementation for the paper Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Contro

MIT Graphics Group 65 Jan 07, 2023
A plug-and-play library for neural networks written in Python

A plug-and-play library for neural networks written in Python!

Dimos Michailidis 2 Jul 16, 2022
Code for Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing(ICCV21)

NeuralGIF Code for Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing(ICCV21) We present Neural Generalized Implicit F

Garvita Tiwari 104 Nov 18, 2022
Framework web SnakeServer.

SnakeServer - Framework Web 🐍 Documentação oficial do framework SnakeServer. Conteúdo Sobre Como contribuir Enviar relatórios de segurança Pull reque

Jaedson Silva 0 Jul 21, 2022
Tensorflow implementation of our method: "Triangle Graph Interest Network for Click-through Rate Prediction".

TGIN Tensorflow implementation of our method: "Triangle Graph Interest Network for Click-through Rate Prediction". Files in the folder dataset/ electr

Alibaba 21 Dec 21, 2022