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/WaterScenes/Awesome-Water-Surface-Perception

WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces

https://github.com/Watson-BCA/HumanPostureRecognition

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/WavesUR/embedded_TSM

Non-local Neural Networks

https://github.com/Will-J-Gale/Self-Driving-Car-Vision

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Windxy/YOLOv4-Based-on-PaddleDetection

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/WinstonLy/Electricity-Inspection-Based-Ascend310

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/WonJunPark/Efficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/WordBearerYI/VoxelNet-pytorch

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

https://github.com/Wordbe/MobileNetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/WuJiang1996/pytorch-YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/WuPedin/Multi-class_Yolov5_DeepSort_Pytorch

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/X-BrainLab/GTNet

GTNet: Generative Transfer Network for Zero-Shot Object Detection

https://github.com/XJTUWYD/TWN

Ternary Weight Networks

https://github.com/XXXVincent/SOLO

SOLOv2: Dynamic and Fast Instance Segmentation

https://github.com/XavierCHEN34/ClickSEG

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/Xephyrum/Agriculturice

Scalable Object Detection using Deep Neural Networks

https://github.com/Xephyrum/CropsMD

Scalable Object Detection using Deep Neural Networks

https://github.com/XiangGaofeng/MindSpore-SSD

SSD: Single Shot MultiBox Detector

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

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