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/JennyVanessa/Paddle-GHM

Gradient Harmonized Single-stage Detector

https://github.com/JensSettelmeier/EfficientDet-DeepSORT-Tracker

EfficientDet: Scalable and Efficient Object Detection

https://github.com/Jeongseungwoo/U_net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Jeongyun-Lee-0423/Classification

Deep Residual Learning for Image Recognition

https://github.com/Jeremy-lf/RT-DATR

RT-DATR:Real-time Unsupervised Domain Adaptive Detection Transformer with Adversarial Feature Learning

https://github.com/JesonZhang822/Dogs-VS-Cats-Project

Deep Residual Learning for Image Recognition

https://github.com/JetRunner/PABEE

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/JeyesHan/P2LR

Deep Residual Learning for Image Recognition

https://github.com/Jia-Research-Lab/GridMask

GridMask Data Augmentation

https://github.com/JiaPeng1234/MRI-Segmentation-Transformer

Non-local Neural Networks

https://github.com/Jiachenyin1/Yolov3-U-attention-permuting

YOLOv3: An Incremental Improvement

https://github.com/JialeCao001/D2Det

https://github.com/JialeCao001/HSD

https://github.com/JiamingLv/WKD

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation

https://github.com/JiangWenPL/multiperson

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/JiaweiZhao-git/DASNet

Is Depth Really Necessary for Salient Object Detection?

https://github.com/Jiaxin-Ye/DepMamba

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

https://github.com/JifeiWang-WHU/Pytorch_Building_extraction

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JimmyLauren/caffe_ssd_mofified

SSD: Single Shot MultiBox Detector

https://github.com/JinLi711/Attention-Augmented-Convolution

CBAM: Convolutional Block Attention Module

https://github.com/JinLi711/Convolution_Variants

CBAM: Convolutional Block Attention Module

https://github.com/JinfengZhang1994/DynaSLAM

DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

https://github.com/JingtongSu/sanity-checking-pruning

SNIP: Single-shot Network Pruning based on Connection Sensitivity

https://github.com/Jintao-Huang/Darknet53_PyTorch

YOLOv3: An Incremental Improvement

https://github.com/Jintao-Huang/EfficientDet_PyTorch

EfficientDet: Scalable and Efficient Object Detection

https://github.com/Jintao-Huang/EfficientNet_PyTorch

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/JinyongJeong/DeeplabV3_Apolloscape_and_CityScape

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/JinyuanLiu-CV/TarDAL

Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection

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

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