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/dhu-mmct/dhu-mmct

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/diasirish/mobilenetv3

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/digantamisra98/Reproducibilty-Challenge-ECANET

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

https://github.com/dimahwang88/mmdetection-multigpu-train

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/dineshzende/awesome-deep-learning-resources

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/dingjiansw101/RoITransformer_DOTA

Learning RoI Transformer for Detecting Oriented Objects in Aerial Images

https://github.com/dingwoai/FairMOT-BDD100K

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/dipuchak95/dip_net

YOLOv3: An Incremental Improvement

https://github.com/direcf/pointrcnn_multiclass

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

https://github.com/dishen12/py03

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/dispink/xpt

Masked Autoencoders Are Scalable Vision Learners

https://github.com/divamgupta/image-segmentation-keras

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/divyachandana/solo-semantic-segmentation

SOLOv2: Dynamic and Fast Instance Segmentation

https://github.com/divyam96/EESRGAN

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

https://github.com/diyer22/bpycv

Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization

https://github.com/djcustard/GestureGAN

Going Deeper with Convolutions

https://github.com/djkim3/Yet-Another-EfficientDet-Pytorch

EfficientDet: Scalable and Efficient Object Detection

https://github.com/dmizr/phuber

Deep Residual Learning for Image Recognition

https://github.com/dmlc/dgl/tree/master/examples/mxnet/monet

Geometric deep learning on graphs and manifolds using mixture model CNNs

https://github.com/dmlc/dgl/tree/master/examples/pytorch/monet

Geometric deep learning on graphs and manifolds using mixture model CNNs

https://github.com/dmonterom/face_recognition_TF2

Group Normalization

https://github.com/dnddnjs/pytorch-cifar10

Deep Residual Learning for Image Recognition

https://github.com/dnozza/profanity-obfuscation

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/do4am/MultiviewFaceDetector

Multi-view Face Detection Using Deep Convolutional Neural Networks

https://github.com/doc-doc/NExT-OE

PaLI-X: On Scaling up a Multilingual Vision and Language Model

https://github.com/docling-project/docling

DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis

https://github.com/doheelab/pku-CenterNet

Objects as Points

https://github.com/dominickrei/limited-data-vits

Masked Autoencoders Are Scalable Vision Learners

https://github.com/donghang941114/mmdetection_my

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/dongkyuk/ConvNext-tensorflow

A ConvNet for the 2020s

https://github.com/dongkyuk/PatchConvNet-pytorch

Augmenting Convolutional networks with attention-based aggregation

https://github.com/dongkyuk0419/YOLO

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

https://github.com/dooleys/robustness

TinaFace: Strong but Simple Baseline for Face Detection

https://github.com/dorbodwolf/darknet

YOLOv3: An Incremental Improvement

https://github.com/dragon20002/STLC_Server

YOLO9000: Better, Faster, Stronger

https://github.com/dravenww/curated-article

Masked Autoencoders Are Scalable Vision Learners

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