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/2023-MindSpore-1/ms-code-194

YOLOX: Exceeding YOLO Series in 2021

https://github.com/2023-MindSpore-1/ms-code-195

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/2023-MindSpore-1/ms-code-201

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-202

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

https://github.com/2023-MindSpore-1/ms-code-206

Masked Autoencoders Are Scalable Vision Learners

https://github.com/2023-MindSpore-1/ms-code-207

Mask R-CNN

https://github.com/2023-MindSpore-1/ms-code-208

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-21

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/2023-MindSpore-1/ms-code-210/tree/main/cnn_direction_model

Structured Sparse R-CNN for Direct Scene Graph Generation

https://github.com/2023-MindSpore-1/ms-code-211/tree/main/Deepsort

StrongSORT: Make DeepSORT Great Again

https://github.com/2023-MindSpore-1/ms-code-211/tree/main/detr

DETRs with Hybrid Matching

https://github.com/2023-MindSpore-1/ms-code-212/tree/main/EfficientDet_d0

EfficientDet: Scalable and Efficient Object Detection

https://github.com/2023-MindSpore-1/ms-code-212/tree/main/efficientnet-b0

An Efficient and Scalable Deep Learning Approach for Road Damage Detection

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/EGnet

EGNet:Edge Guidance Network for Salient Object Detection

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FCANet

FcaNet: Frequency Channel Attention Networks

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/faster_rcnn_dcn

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

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/faster_rcnn_ssod

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

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HMR

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_seg

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HireMLP

Hire-MLP: Vision MLP via Hierarchical Rearrangement

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/frustum-pointnet

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

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/googlenet

Going Deeper with Convolutions

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/hardnet

HarDNet: A Low Memory Traffic Network

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/ibnnet

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

https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PSPNet

Pyramid Scene Parsing Network

https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointpillars

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointtransformer

Point Transformer

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/PVAnet

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RefineDet

RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/ResNeSt50

ResNeSt: Split-Attention Networks

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/SE-Net

Squeeze-and-Excitation Networks

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/SE_ResNeXt50

Squeeze-and-Excitation Networks

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net

Res2Net: A New Multi-scale Backbone Architecture

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