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/TusharMhaske28/summer-project

YOLOv3: An Incremental Improvement

https://github.com/UBC-CIC/COVID19-L3-Net

Pyramid Scene Parsing Network

https://github.com/UESTC-Liuxin/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

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

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

https://github.com/USTC-IMCL/HST-for-Compressed-SR

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/Ugenteraan/Masked-AutoEncoder-PyTorch

Masked Autoencoders Are Scalable Vision Learners

https://github.com/Ugenteraan/YOLOv1

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

https://github.com/Ugness/PiCANet-Implementation

PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection

https://github.com/Unity-Technologies/SynthDet

An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection

https://github.com/UnofficialJuliaMirror/DeepMark-deepmark

Deep Residual Learning for Image Recognition

https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark

Deep Residual Learning for Image Recognition

https://github.com/Uranium-Deng/Steganalysis-StegoRemoval

CBAM: Convolutional Block Attention Module

https://github.com/UsmannK/cloud-mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/UtkarshKharche29/Automatic-Number-Plate-Recognition

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Uuuuuuho/Pillar_on_SoC

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/V3Det/Detectron2-V3Det

V3Det: Vast Vocabulary Visual Detection Dataset

https://github.com/V3Det/mmdetection-V3Det

RTMDet: An Empirical Study of Designing Real-Time Object Detectors

https://github.com/VCIP-RGBD/DFormer

DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation

https://github.com/VCasecnikovs/Yet-Another-YOLOv4-Pytorch

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/VLTSeg/VLTSeg

Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning

https://github.com/VaanHUANG/CSCI5210HW1

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

https://github.com/VainF/DeepLabV3Plus-Pytorch

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Vamshi399/CarND-Vehicle-Detection

Deep Residual Learning for Image Recognition

https://github.com/VamsiKrishna1211/gender_age-detection

Deep Residual Learning for Image Recognition

https://github.com/VasuTammisetti/Object-detection-using-YOLO_V3-Darknet

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/VcampSoldiers/Swin-Transformer-Tensorflow

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/VedantDere0104/Pix2Pix_GAN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Veincore/f-pointnet

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

https://github.com/VeritasXu/Ternary-Federated

Ternary Weight Networks

https://github.com/Veternal1226/YOLO_darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/VhalPurohit/290s

Deep Residual Learning for Image Recognition

https://github.com/ViTAE-Transformer/ViTAE-Transformer

ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias

https://github.com/ViTAE-Transformer/ViTDet

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/Video-Streaming-Pipeline/Video-Streaming-Pipeline

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Vignesh-95/cnn-semantic-segmentation-satellite-images

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Vijayabhaskar96/Object-Detection-Algorithms

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

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