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/XiangbingJi/Stanford-cs230-final-project

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/XinYangDong/ttfnet-master2

Training-Time-Friendly Network for Real-Time Object Detection

https://github.com/Xingyu-Romantic/RMPose_PAF

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/XuLena/3D-sensing

nuScenes: A multimodal dataset for autonomous driving

https://github.com/XuanjieXiao/vehicle-detection-by-using-camera

YOLO9000: Better, Faster, Stronger

https://github.com/Xunpeng746/ACMo

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

https://github.com/YC-Lai/Sequential-DDETR

Deformable DETR: Deformable Transformers for End-to-End Object Detection

https://github.com/YCHung1998/Object-Detection

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/YIZHE12/fashiontags

Focal Loss for Dense Object Detection

https://github.com/YIZHE12/robots

Focal Loss for Dense Object Detection

https://github.com/YONGQUAN-QU/CBAM.Flax

CBAM: Convolutional Block Attention Module

https://github.com/Yamahitsuji/darknet

YOLOv3: An Incremental Improvement

https://github.com/Yan107351111/SSOD

Learning to Detect and Retrieve Objects from Unlabeled Videos

https://github.com/Yang-Q-D/mindsporeCode/tree/master/ibnnet

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/Yang-YiFan/DiracDeltaNet

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/Yang2446/pointnet

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/Yanghojun/Custom_yolov5_pytorch

StrongSORT: Make DeepSORT Great Again

https://github.com/YangtaoWANG95/TokenCut

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

https://github.com/Yashgh7076/CU-Thesis

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Yashgh7076/Eluvio-ML-Scene-Segmentation

Focal Loss for Dense Object Detection

https://github.com/Yc174/frustum-pointnets

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

https://github.com/YeLyuUT/MMDET

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/YeLyuUT/VISLNet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/YeongHyeon/ReXNet-TF2

Rethinking Channel Dimensions for Efficient Model Design

https://github.com/YeongHyeon/ResNeSt-TF2

ResNeSt: Split-Attention Networks

https://github.com/YigeunLee/fcn32

Fully Convolutional Networks for Semantic Segmentation

https://github.com/Yijunmaverick/GenerativeFaceCompletion

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/Yimin-Yuan/VeinProject

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/YinDFY/PGD_for_targeted_attack

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/YininKorea/Contour-aware-equipotential-learning

Pyramid Scene Parsing Network

https://github.com/YiruS/pointnet_adversarial

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/Yonder-OSS/D3M-Primitives

Focal Loss for Dense Object Detection

https://github.com/YongWookHa/swin-transformer-ocr

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/Yongjjun/singleshotpose

BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth

https://github.com/Yotonctu/densefusion_torch1.0

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

https://github.com/YoungXIAO13/FewShotDetection

Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild

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