Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/code-implementation1/Code4/tree/main/HireMLP

Hire-MLP: Vision MLP via Hierarchical Rearrangement

https://github.com/code-implementation1/Code4/tree/main/ISyNet

ISyNet: Convolutional Neural Networks design for AI accelerator

https://github.com/code-implementation1/Code4/tree/main/IndexNet

InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting

https://github.com/code-implementation1/Code4/tree/main/hed

HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

https://github.com/code-implementation1/Code4/tree/main/hlg

Vision Transformers: From Semantic Segmentation to Dense Prediction

https://github.com/code-implementation1/Code4/tree/main/ibnnet

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

https://github.com/code-implementation1/Code4/tree/main/libra-rcnn

Libra R-CNN: Towards Balanced Learning for Object Detection

https://github.com/code-implementation1/Code4/tree/main/lite-hrnet

HRNET: AI on Edge for mask detection and social distancing

https://github.com/code-implementation1/Code5/tree/main/lresnet100e_ir

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code5/tree/main/m2det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

https://github.com/code-implementation1/Code5/tree/main/mnasnet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/code-implementation1/Code5/tree/main/ms_rcnn

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

https://github.com/code-implementation1/Code6/tree/main/Non_local

Non-local Neural Networks

https://github.com/code-implementation1/Code6/tree/main/PSPNet

Pyramid Scene Parsing Network

https://github.com/code-implementation1/Code6/tree/main/PVAnet

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

https://github.com/code-implementation1/Code6/tree/main/nas-fpn

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

https://github.com/code-implementation1/Code6/tree/main/nasnet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/code-implementation1/Code6/tree/main/pointpillars

Sparse PointPillars: Maintaining and Exploiting Input Sparsity to Improve Runtime on Embedded Systems

https://github.com/code-implementation1/Code6/tree/main/pointtransformer

Point Transformer

https://github.com/code-implementation1/Code7/tree/main/RefineDet

Single-Shot Refinement Neural Network for Object Detection

https://github.com/code-implementation1/Code7/tree/main/ResNeXt

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/code-implementation1/Code7/tree/main/SE_ResNeXt50

Squeeze-and-Excitation Networks

https://github.com/code-implementation1/Code7/tree/main/rcnn

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

https://github.com/code-implementation1/Code7/tree/main/res2net

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/code-implementation1/Code7/tree/main/res2net_deeplabv3

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/code-implementation1/Code7/tree/main/res2net_faster_rcnn

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

https://github.com/code-implementation1/Code7/tree/main/res2net_yolov3

YOLOX: Exceeding YOLO Series in 2021

https://github.com/code-implementation1/Code7/tree/main/resnetv2

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code7/tree/main/resnext152_64x4d

Panoptic Feature Pyramid Networks

https://github.com/code-implementation1/Code7/tree/main/retinanet_resnet101

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/code-implementation1/Code7/tree/main/retinanet_resnet152

Stand-Alone Self-Attention in Vision Models

https://github.com/code-implementation1/Code7/tree/main/rfcn

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/code-implementation1/Code8/tree/main/Segformer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/code-implementation1/Code8/tree/main/SpineNet

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_mobilenetv1

Mask R-CNN

https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_resnet50

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

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