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/BR-IDL/PaddleViT/blob/main/object_detection/DETR

End-to-End Object Detection with Transformers

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification

ConvMLP: Hierarchical Convolutional MLPs for Vision

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/CoaT

Co-Scale Conv-Attentional Image Transformers

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ConvNeXt

A ConvNet for the 2020s

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/Focal_Transformer

Focal Self-attention for Local-Global Interactions in Vision Transformers

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/HaloNet

Scaling Local Self-Attention for Parameter Efficient Visual Backbones

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/MobileOne

MobileOne: An Improved One millisecond Mobile Backbone

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/MobileViT

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/PiT

Rethinking Spatial Dimensions of Vision Transformers

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/XCiT

XCiT: Cross-Covariance Image Transformers

https://github.com/BR-IDL/PaddleViT/tree/develop/self_supervised_learning/dino

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/BUPT-PRIV/MAE-priv

Masked Autoencoders Are Scalable Vision Learners

https://github.com/Baibaifan/An-Improved-PeleeNet-Algorithm-with-Feature-Pyramid-Networks-for-Image-Detection-

Feature Pyramid Networks for Object Detection

https://github.com/Baichenjia/Resnet

Deep Residual Learning for Image Recognition

https://github.com/BaiqingL/CS523-Final

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Bakikii/stylegan2-pytorch23

Efficient Attention: Attention with Linear Complexities

https://github.com/BaladityaY/lemniscate_reproduce_bdd

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/BaladityaY/lemniscate_vis

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/Bangbangbanana/fairmot_mindspore

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

https://github.com/Banus/caffe-demo

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

https://github.com/Bao-Jiarong/ResNet

Deep Residual Learning for Image Recognition

https://github.com/Barak123748596/CVDL_course_project

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/BboyHanat/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/BearCooike/ssFPN-pytorch

ssFPN: Scale Sequence (S^2) Feature Based-Feature Pyramid Network for Object Detection

https://github.com/Beaver48/kaggle-chest-xray-abnormalities

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/BebDong/MXNetSeg

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Ben-Louis/FisherPruning-Pytorch

Group Fisher Pruning for Practical Network Compression

https://github.com/BenjaminLiuPenrose/UnsupervisedImage

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/BenjiKCF/EfficientNet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/BenjiKCF/Superesolution-and-watermark-removal

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Bennie-Han/Image-augementation-pytorch

Training Region-based Object Detectors with Online Hard Example Mining

https://github.com/Benzlxs/benzlxs.github.io

Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes

https://github.com/BerkeleyLearnVerify/VerifAI

Counterexample-Guided Data Augmentation

https://github.com/BertaBescos/DynaSLAM

DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

https://github.com/Biantian/MscProject

Mask R-CNN

https://github.com/Bigpingping97/ICNet

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

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