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/DecentMakeover/detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/DecentMakeover/mdetect

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Deci-AI/super-gradients

Going Deeper with Convolutions

https://github.com/Deep-Learner/Plot_CNNs_Python_Latex

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

https://github.com/DeepBrainsMe/FSnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/DeepMark/deepmark

Deep Residual Learning for Image Recognition

https://github.com/DeepTrial/Retina-VesselNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DeepVoltaire/Fast-SCNN

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/Deeplodocus/COCO-with-YOLO

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

https://github.com/Demonhesusheng/darknet_v2

YOLOv3: An Incremental Improvement

https://github.com/DengPingFan/CODToolbox

https://github.com/DengPingFan/DAVSOD

https://github.com/DengPingFan/SINet

https://github.com/DentanJeremie/adversarialTransferts

Task-generalizable Adversarial Attack based on Perceptual Metric

https://github.com/DequanWang/tent

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

https://github.com/DerrickGXD/unsupervised-keypoints

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/DerryHub/ByteTrack-attack

Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers

https://github.com/DetectionTeamUCAS/FCOS_GluonCV

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/DetectionTeamUCAS/FPN_Tensorflow

Feature Pyramid Networks for Object Detection

https://github.com/DetectionTeamUCAS/R2CNN_Faster-RCNN_Tensorflow

Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

https://github.com/DetectionTeamUCAS/RRPN_Faster-RCNN_Tensorflow

Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

https://github.com/DetectionTeamUCAS/RRPN_Faster_RCNN_Tensorflow

Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation

Focal Loss for Dense Object Detection

https://github.com/DevBruce/PyTorch-ISBI2012-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DevBruce/Segmentation-UNet-PyTorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DevBruce/YOLO-TF2

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

https://github.com/DevBruce/YOLOv1-TF2

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

https://github.com/DevBruce/YOLOv3-TF2

YOLOv3: An Incremental Improvement

https://github.com/DevPranjal/reproduction-review-kd

Distilling Knowledge via Knowledge Review

https://github.com/DhruvMakwana/Global-Wheat-Detection

Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods

https://github.com/Di-Is/stylegan2-ada-pytorch

Efficient Attention: Attention with Linear Complexities

https://github.com/DiMarzioRock7/Aerial-Semantic-Segmentation-Drone

Pyramid Scene Parsing Network

https://github.com/DiMarzioRock7/PSPNet

Pyramid Scene Parsing Network

https://github.com/DianaSHV/lemniscate_edit

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/DiegoArcelli/De-Stylization-Network

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

https://github.com/Diooooo/Reimplement-HED

Holistically-Nested Edge Detection

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