Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

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

https://github.com/JisuHann/MEME-Tracking

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/JisuHann/Object-Tracking

YOLOv4: Optimal Speed and Accuracy of Object Detection

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

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

https://github.com/Jittor-Image-Models/Jittor-Image-Models

Visual Attention Network

https://github.com/Jittor/InstanceSegmentation-jittor

Pose2Seg: Detection Free Human Instance Segmentation

https://github.com/Jmak12/Iris1

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Jo-dsa/SemanticSeg

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JoeTracey/Portfolio

Deep Residual Learning for Image Recognition

https://github.com/JoegameZhou/efficientnet-b0

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/JoegameZhou/hrnetv2

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

https://github.com/JoegameZhou/mPanGu-Alpha-53

PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

https://github.com/JoegameZhou/ssd

SSD: Single Shot MultiBox Detector

https://github.com/JoelPendleton/Edge-Detection-CNN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JoelPendleton/Object-Contour-Detection-CNN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Jonashellevang/Weapons_Detection_YOLOv3

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Jongchan/attention-module

CBAM: Convolutional Block Attention Module

https://github.com/Jonydom/Faster-RCNN

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

https://github.com/Jorge-Mendes/darknet-google-colab

YOLOv3: An Incremental Improvement

https://github.com/JosephPB/XNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JoshuaPlacidi/MS-COCO-Object-Tags

VinVL: Revisiting Visual Representations in Vision-Language Models

https://github.com/JoshuaPlacidi/MS-COCO-Tags

VinVL: Revisiting Visual Representations in Vision-Language Models

https://github.com/JoyHsiao/Haitec

SSD: Single Shot MultiBox Detector

https://github.com/Joyce511/OpenVino_Realtime_Multi-Person_Pose_Estimation

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

https://github.com/JuliaChae/M3D-RPN-Waymo

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

https://github.com/JunaidCS032/MOTBeyondPixels

Accurate Single Stage Detector Using Recurrent Rolling Convolution

https://github.com/JungminChung/Korean_Struc_Det

Objects as Points

https://github.com/JunnYu/x-transformers-paddle

End-to-End Object Detection with Transformers

https://github.com/JunshengFu/vehicle-detection

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

https://github.com/JunweiLiang/Object_Detection_Tracking

Relation Networks for Object Detection

https://github.com/JunweiLiang/aicity_action

MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

https://github.com/K-Mike/Automatic-salt-deposits-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/KGML-lab/Generalized-Forward-Inverse-Framework-for-DL4SI

MEMTRACK: A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments

https://github.com/KMnP/fashionpedia-api

Mask R-CNN

https://github.com/KPMG-wiseuniv/AI

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/KUR-creative/SickZil-Machine

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/KaFaiFai/MobileNet-PyTorch

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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