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/Vill-Lab/2022-TIP-HCGA

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/VinayBN8997/ResNet-CIFAR10

Deep Residual Learning for Image Recognition

https://github.com/VinayBhupalam/melanoma-detection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/VinishUchiha/Object-Detection-Using-Yolo4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Violet981/Breast_mass_Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/VisDrone/DroneVehicle

Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning

https://github.com/VishaalMK/VectorDefense

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/Vishal-V/tf-models

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

https://github.com/VisionLearningGroup/DA_Detection

Strong-Weak Distribution Alignment for Adaptive Object Detection

https://github.com/Visual-AI/Mr.DETR

Mr. DETR: Instructive Multi-Route Training for Detection Transformers

https://github.com/Visual-Attention-Network/VAN-Jittor

Visual Attention Network

https://github.com/Visual-Attention-Network/VAN-Segmentation

Visual Attention Network

https://github.com/Visual-Behavior/aloception

End-to-End Object Detection with Transformers

https://github.com/Visual-Behavior/detr-tensorflow

End-to-End Object Detection with Transformers

https://github.com/VisualComputingInstitute/SiamR-CNN

Siam R-CNN: Visual Tracking by Re-Detection

https://github.com/ViswanathaReddyGajjala/EfficientNet-RetinaNet

Deep Residual Learning for Image Recognition

https://github.com/VivekUdupa/CPSC8810-DeepLearning_vkoodli_shonnah

Contextual Action Recognition with R*CNN

https://github.com/Viveksbawa/SARAS-ESAD-Baseline

SSD: Single Shot MultiBox Detector

https://github.com/Vonski/wdi19

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

https://github.com/WARM-3D/WARM-3D

WARM-3D: A Weakly-Supervised Sim2Real Domain Adaptation Framework for Roadside Monocular 3D Object Detection

https://github.com/WWangYuHsiang/SMILEtrack

SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

https://github.com/WXinlong/DenseCL

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

https://github.com/WYGNG/CenterNet-Pro

Objects as Points

https://github.com/WYGNG/USTC_SSE_Project

Objects as Points

https://github.com/WaelOuni/MergeTenserFlowWithOdb

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

https://github.com/Wakeupbuddy/amodalAPI

Semantic Amodal Segmentation

https://github.com/WalterMa/gluon-faster-rcnn

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

https://github.com/WangFeng18/Swin-Transformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/WangMian-Maker/Flask

YOLOv3: An Incremental Improvement

https://github.com/WangWenhao0716/Adapted-Center-and-Scale-Prediction

Adapted Center and Scale Prediction: More Stable and More Accurate

https://github.com/WangWenhao0716/DomainMix

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

https://github.com/WangY0906/mmdetection-for-study

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Wangxy2180/darknetKinectDetect

YOLO9000: Better, Faster, Stronger

https://github.com/WangyiNTU/SCALNet

A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds

https://github.com/Wastoon/MOT-CenterNet

Weighted boxes fusion: Ensembling boxes from different object detection models

https://github.com/Wastoon/XinTong_CenterNet

Objects as Points

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