Papers with Code 아카이브

Object Detection

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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/clemsgrs/hipt

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/cleverhans-lab/cleverhans

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/clip-italian/clip-italian

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/clive819/Modified-DETR

End-to-End Object Detection with Transformers

https://github.com/cloudnine148/PHC_2nd_SPC

YOLO9000: Better, Faster, Stronger

https://github.com/cmf3673/spotti

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/cmhungsteve/Awesome-Transformer-Attention

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

https://github.com/cmitash/physim-dataset-generator

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation

https://github.com/coconutzs/PointNet_zs

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

https://github.com/cocopambag/ResNet

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code1/tree/main/Deepsort

StrongSORT: Make DeepSORT Great Again

https://github.com/code-implementation1/Code1/tree/main/EfficientDet_d0

EfficientDet: Scalable and Efficient Object Detection

https://github.com/code-implementation1/Code1/tree/main/detr

DETRs with Collaborative Hybrid Assignments Training

https://github.com/code-implementation1/Code2

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/code-implementation1/Code3/tree/main/EGnet

https://github.com/code-implementation1/Code3/tree/main/FaceAttribute

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code3/tree/main/efficientnet-b0

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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/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

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