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/ganguli-lab/Synaptic-Flow

SNIP: Single-shot Network Pruning based on Connection Sensitivity

https://github.com/gaobb/DCFS

Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation

https://github.com/gaopengcuhk/Pretrained-Pix2Seq

Pix2seq: A Language Modeling Framework for Object Detection

https://github.com/gaopengcuhk/Stable-Pix2Seq

Pix2seq: A Language Modeling Framework for Object Detection

https://github.com/gaopengcuhk/Unofficial-Pix2Seq

Pix2seq: A Language Modeling Framework for Object Detection

https://github.com/garrettruss/ImageRecognitionReact

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/garrickbrazil/kinematic3d

Kinematic 3D Object Detection in Monocular Video

https://github.com/gary-kaitung/data-science-portfolio

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

https://github.com/gasvn/Res2Net

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/gatheluck/PatchGaussian

Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation

https://github.com/gatheluck/Stronghold

Deep Residual Learning for Image Recognition

https://github.com/gau-nernst/CenterNet

CornerNet: Detecting Objects as Paired Keypoints

https://github.com/gaurav67890/ScaledYOLO

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/gaurav67890/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/gcr/torch-residual-networks

Deep Residual Learning for Image Recognition

https://github.com/gdaosu/awesome-pointcloudregistration

nuScenes: A multimodal dataset for autonomous driving

https://github.com/gdlg/panoramic-depth-estimation

Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery

https://github.com/geekswaroop/Human-Parsing

Pyramid Scene Parsing Network

https://github.com/gene-chou/EFPN-detectron2

Extended Feature Pyramid Network for Small Object Detection

https://github.com/geodekid/frnn

Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

https://github.com/georgeyiasemis/U-Net-PyTorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/gerardrbentley/Pytorch-U-Net-AutoEncoder

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/gfjiangly/CenterNet

Objects as Points

https://github.com/gfursin/browser-extension-for-reproducible-research

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/gfursin/ck-browser-addon

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/gfursin/ck-browser-addon-firefox

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/ghadahamed/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/ghamarian/rfcn

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

https://github.com/ghaniskn/GCorp-Darknet

YOLO9000: Better, Faster, Stronger

https://github.com/ghimiredhikura/Complex-YOLO-V3

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/ghimiredhikura/Complex-YOLOv3

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/giannifranchi/deeplabv3-superpixelmix

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/giantseaweed/decree

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/gihants/darknet_built

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/ginn24/Pelee-TensorRT

Pelee: A Real-Time Object Detection System on Mobile Devices

https://github.com/ginn24/RefineDet-1

Single-Shot Refinement Neural Network for Object Detection

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