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/liuxingwt/CLS

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/liuyao12/ConvNets-PDE-perspective

Deep Residual Learning for Image Recognition

https://github.com/liuyuan000/yolox_sar

YOLOX: Exceeding YOLO Series in 2021

https://github.com/liuziyu77/mmdu

RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition

https://github.com/livox-sdk/livox_detection

Center-based 3D Object Detection and Tracking

https://github.com/lixiaolei1982/DenseNet-base-Mask-RCNN-for-Human-Pose-Estimation

Mask R-CNN

https://github.com/lixiaoyu2000/fastpoly

RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework

https://github.com/lixihan/Resnet

Deep Residual Learning for Image Recognition

https://github.com/lixixibj/forecasting-with-time-series-imaging

Going Deeper with Convolutions

https://github.com/lixixibj/tsImage-forecasting

Going Deeper with Convolutions

https://github.com/liyemei/Deformable-active-convolutional

Deformable Convolutional Networks

https://github.com/lizhe960118/CenterNet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/lizhe960118/HrCenterNet

CenterNet: Keypoint Triplets for Object Detection

https://github.com/ljjyxz123/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/ljllili23/unet_baseline

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ljy-hy/mentormix_pytorch

Deep Residual Learning for Image Recognition

https://github.com/lkeonwoo94/DL_cv-mdt_NVIDIA_Cert_Course_StudyPI

Deep Residual Learning for Image Recognition

https://github.com/llsouder/screen-ocr

Focal Loss for Dense Object Detection

https://github.com/llzlcl/pointcloud-segment

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

https://github.com/lmeulen/PeopleCounter

YOLOv3: An Incremental Improvement

https://github.com/lmk123568/OD_Confluence

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

https://github.com/lncarter/Openpose

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

https://github.com/loadbxh/yolov3

YOLOv3: An Incremental Improvement

https://github.com/lobznet/yolov3

YOLOv3: An Incremental Improvement

https://github.com/locuslab/convex_adversarial

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/locuslab/robust_overfitting

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/loes5307/vocaladversary2022

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/logiklesuraj/SiamFC

Fully-Convolutional Siamese Networks for Object Tracking

https://github.com/logiklesuraj/siamfcex

Fully-Convolutional Siamese Networks for Object Tracking

https://github.com/lojzezust/WaSR

MODS -- A USV-oriented object detection and obstacle segmentation benchmark

https://github.com/long95288/car-logo-detect

YOLOv3: An Incremental Improvement

https://github.com/longcw/roialign.pytorch

Mask R-CNN

https://github.com/longcw/yolo2-pytorch

YOLO9000: Better, Faster, Stronger

https://github.com/longzw1997/Open-GroundingDino

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/lonnyzhang423/github-hot-hub

You Only Learn One Representation: Unified Network for Multiple Tasks

https://github.com/lornatang/googlenet-pytorch

Going Deeper with Convolutions

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