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/Luckygyana/Invo-AI

Weighted boxes fusion: Ensembling boxes from different object detection models

https://github.com/LuigiRussoDev/Covid19Detection

Deep Residual Learning for Image Recognition

https://github.com/LuigiRussoDev/ResNets

Deep Residual Learning for Image Recognition

https://github.com/LukeBolly/tf-tspn

Conditional Set Generation with Transformers

https://github.com/Lw510107/PointNet

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

https://github.com/Lw510107/pointnet-2018.6.27-

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

https://github.com/Lxrd-AJ/Advanced_ML

Pyramid Scene Parsing Network

https://github.com/MECLabTUDA/ACS

Incremental Learning Techniques for Semantic Segmentation

https://github.com/MEME-Phoenix/Autonomous-Driving-Cart-MEME

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/MEME-Phoenix/MEME

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/MHersche/eegnet-based-embedded-bci

CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams

https://github.com/MIC-DKFZ/DetectionAndRegression

Mask R-CNN

https://github.com/MIC-DKFZ/RegRCNN

Mask R-CNN

https://github.com/MINED30/Face_Mask_Detection_YOLO

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

https://github.com/MIT-SPARK/CertifiablyRobustPerception

TEASER: Fast and Certifiable Point Cloud Registration

https://github.com/MIhappen/CaffeSourceCode

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

https://github.com/MRZWD/yolov3_caltech

YOLOv3: An Incremental Improvement

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/dpn

DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/edgenext

EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/googlenet

Going Deeper with Convolutions

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/mnasnet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/mobilenetv1

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/mobilenetv2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/poolformer

MetaFormer Is Actually What You Need for Vision

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/pvt

PVT v2: Improved Baselines with Pyramid Vision Transformer

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/pvt_v2

PVT v2: Improved Baselines with Pyramid Vision Transformer

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/res2net

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnest

ResNeSt: Split-Attention Networks

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnet

Deep Residual Learning for Image Recognition

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/rexnet

Rethinking Channel Dimensions for Efficient Model Design

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/senet

Squeeze-and-Excitation Networks

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/shufflenet_v1

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/swintransformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/vit

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/xcit

XCiT: Cross-Covariance Image Transformers

https://github.com/MS-Mind/MS-Code-06/tree/main/Non_local

Non-local Neural Networks

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