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/Yashgh7076/Eluvio-ML-Scene-Segmentation

Focal Loss for Dense Object Detection

https://github.com/Yc174/frustum-pointnets

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

https://github.com/YeLyuUT/MMDET

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/YeLyuUT/VISLNet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/YeongHyeon/ReXNet-TF2

Rethinking Channel Dimensions for Efficient Model Design

https://github.com/YeongHyeon/ResNeSt-TF2

ResNeSt: Split-Attention Networks

https://github.com/Yimin-Yuan/VeinProject

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/YinDFY/PGD_for_targeted_attack

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/YininKorea/Contour-aware-equipotential-learning

Pyramid Scene Parsing Network

https://github.com/YiruS/pointnet_adversarial

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

https://github.com/Yonder-OSS/D3M-Primitives

Focal Loss for Dense Object Detection

https://github.com/YongWookHa/swin-transformer-ocr

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/Yongjjun/singleshotpose

BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth

https://github.com/Yotonctu/densefusion_torch1.0

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

https://github.com/YoungXIAO13/FewShotDetection

Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild

https://github.com/YoungXIAO13/PoseContrast

Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild

https://github.com/YourGc/Unet_jinnan2

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/YoushaaMurhij/FasterCNN_Object_Detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/YudeWang/UNet-Satellite-Image-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/YuefeiZ/PANet

Path Aggregation Network for Instance Segmentation

https://github.com/YuemingJin/TMRNet

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

https://github.com/YufeiHU-fr/obow_ssl

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

https://github.com/YukangWang/DeepFlux

DeepFlux for Skeletons in the Wild

https://github.com/Yuma82/Paper-Survey

ODAM: Gradient-based instance-specific visual explanations for object detection

https://github.com/YunYang1994/tensorflow-yolov3

YOLOv3: An Incremental Improvement

https://github.com/Yuxiang1995/ICDAR2021_MFD

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Yuzi0123/MindSpore_Yolov5

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/YvanG/VQGAN-CLIP

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ZFTurbo/segmentation_models_3D

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ZH-Lee/Tensorflow-Mobilenet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/ZJCV/KnowledgeReview

Distilling Knowledge via Knowledge Review

https://github.com/ZJCV/ZCls

ResNeSt: Split-Attention Networks

https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/fairmot.py

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/ZQPei/deep_sort_pytorch

Mask R-CNN

https://github.com/ZTao-z/multiflow-resnet-ssd

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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