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/DLWK/EANet

Fully Convolutional Networks for Semantic Segmentation

https://github.com/DS4SD/DocLayNet

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

https://github.com/DS4SD/docling

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

https://github.com/DT42/BerryNet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/DableUTeeF/keras-efficientnet

Deep Residual Learning for Image Recognition

https://github.com/DaiJianBo/CenterNet-duan-2080Ti

CenterNet: Keypoint Triplets for Object Detection

https://github.com/DaikiTanak/manifold_mixup

Deep Residual Learning for Image Recognition

https://github.com/Daividao/semantic-segmentation-deeplearning

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DaloroAT/first_break_picking

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Dandelight/VoVNet-PaddlePaddle

An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

https://github.com/Daonancai/unet_muti_class

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DarkGeekMS/Pytorch-YOLOv3-Implementation

YOLOv3: An Incremental Improvement

https://github.com/DarkGeekMS/Semantic_Segmentation_Models_Keras

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DarshanDeshpande/jax-models

Squeeze-and-Excitation Networks

https://github.com/Data-drone/attention_augmented_cnn

Attention Augmented Convolutional Networks

https://github.com/DataCanvasIO/Hypernets

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

https://github.com/DataGenReserarchTeam/expo_markers

Detection and Segmentation of Custom Objects using High Distraction Photorealistic Synthetic Data

https://github.com/DataXujing/Cascade_RCNN_mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/DataXujing/DIoU_YOLO_V3

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/DataXujing/EfficientDet_pytorch

EfficientDet: Scalable and Efficient Object Detection

https://github.com/DataXujing/ExtremeNet-Pytorch

Bottom-up Object Detection by Grouping Extreme and Center Points

https://github.com/DataXujing/TensorRT-DETR

End-to-End Object Detection with Transformers

https://github.com/DataXujing/YOLOR-

You Only Learn One Representation: Unified Network for Multiple Tasks

https://github.com/DataXujing/YOLOX-

YOLOX: Exceeding YOLO Series in 2021

https://github.com/DavHoffmann/LearningToTrainWithSyntheticHumans

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/DavianYang/yolo.ai

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

https://github.com/David-zaiwang/114_rgbd_seg

Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation

https://github.com/David-zaiwang/Image_segmentation_framework

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

https://github.com/DavidCastilloAlvarado/U-NET_AUTOENCODER

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DavidLandup0/deepvision

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/Dayan-Guan/DA-VSN

Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection

https://github.com/DeLightCMU/MAL

Multiple Anchor Learning for Visual Object Detection

https://github.com/DeLightCMU/MAL-inference

Multiple Anchor Learning for Visual Object Detection

https://github.com/DeNA/Chainer_Mask_R-CNN

Mask R-CNN

https://github.com/DeNA/PyTorch_YOLOv3

YOLOv3: An Incremental Improvement

https://github.com/DeadNud1e/Acon

Activate or Not: Learning Customized Activation

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