Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/FarihaHossain/SwinFSR

AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One

https://github.com/FarzamIHPC/YoloV3_mask

YOLOv3: An Incremental Improvement

https://github.com/FateScript/CenterNet-better

Objects as Points

https://github.com/FayCai/framework

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Fei-dong/SOLO_SORT

SOLOv2: Dynamic and Fast Instance Segmentation

https://github.com/FelixGruen/tensorflow-u-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/FenHua/Robust_Logo_Detection

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

https://github.com/Feynman1999/MgeEditing

Tracking Objects as Points

https://github.com/Feynman27/pytorch-detect-rfcn

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/Feynman27/pytorch-detect-to-track

Detect to Track and Track to Detect

https://github.com/FicmillaR/FicmillaR.github.io

Weighted boxes fusion: Ensembling boxes from different object detection models

https://github.com/FlameRB/Face-Detection-using-Deep-Learning

Multi-view Face Detection Using Deep Convolutional Neural Networks

https://github.com/FlashTek/mask-rcnn-edge-agreement-loss

Faster Training of Mask R-CNN by Focusing on Instance Boundaries

https://github.com/FloCF/SSL_pytorch

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/FlorentijnD/FairMOT

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/FlorinAndrei/TensorAim

YOLOv3: An Incremental Improvement

https://github.com/FlowWind1999/pointnet-2

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/FluteXu/ms-project

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/FlyEgle/MAE-pytorch

Masked Autoencoders Are Scalable Vision Learners

https://github.com/FlyingAnt2018/darknet-modify

YOLOv3: An Incremental Improvement

https://github.com/ForrestPi/GHM_Loss

Gradient Harmonized Single-stage Detector

https://github.com/FraCorti/Deep_Subnetworks_for_Dynamic_Resource_Constraints

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/FrancescoSaverioZuppichini/ResNet

Deep Residual Learning for Image Recognition

https://github.com/FrancisCrickInstitute/Etch-a-Cell-Nuclear-Envelope

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/FreddeFrallan/Multilingual-CLIP

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/Fw9wef/DetectorGAN

Generative Modeling for Small-Data Object Detection

https://github.com/G0rav/Automatic_Background_Removal

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/GAIA-vision/GAIA-det

GAIA: A Transfer Learning System of Object Detection that Fits Your Needs

https://github.com/GAOwy123/py-faster-rcnn

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/GH3927/Mask-RCNN-applied-to-cranes

Mask R-CNN

https://github.com/GKalliatakis/Keras-VGG16-places365

Deep Residual Learning for Image Recognition

https://github.com/GT-RIPL/L2C

Learning to Cluster for Proposal-Free Instance Segmentation

https://github.com/GXYM/Focal-loss

Focal Loss for Dense Object Detection

https://github.com/GXYM/GHM_Loss

Gradient Harmonized Single-stage Detector

https://github.com/GZQ0723/YoloV4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Gabriel-Macias/robust_frcnn

A Robust Learning Approach to Domain Adaptive Object Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.