Papers with Code 아카이브

2D Classification

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/WhiskyChoy/language-style-transfer

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/Wikidepia/indonesia_dataset

WikiHow: A Large Scale Text Summarization Dataset

https://github.com/Will-J-Gale/Self-Driving-Car-Vision

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Windxy/YOLOv4-Based-on-PaddleDetection

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/WinstonLy/Electricity-Inspection-Based-Ascend310

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/WonJunPark/Efficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/WordBearerYI/VoxelNet-pytorch

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

https://github.com/Wordbe/MobileNetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/WuJiang1996/pytorch-YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/WuPedin/Multi-class_Yolov5_DeepSort_Pytorch

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/X-BrainLab/GTNet

GTNet: Generative Transfer Network for Zero-Shot Object Detection

https://github.com/XJTUWYD/TWN

Ternary Weight Networks

https://github.com/XXXVincent/SOLO

SOLOv2: Dynamic and Fast Instance Segmentation

https://github.com/XavierCHEN34/ClickSEG

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/Xephyrum/Agriculturice

Scalable Object Detection using Deep Neural Networks

https://github.com/Xephyrum/CropsMD

Scalable Object Detection using Deep Neural Networks

https://github.com/XiSHEN0220/SegSwap

Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

https://github.com/XiangGao1102/CTSS

Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

https://github.com/XiangGaofeng/MindSpore-SSD

SSD: Single Shot MultiBox Detector

https://github.com/XinYangDong/ttfnet-master2

Training-Time-Friendly Network for Real-Time Object Detection

https://github.com/Xingyu-Romantic/RMPose_PAF

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

https://github.com/XinshaoAmosWang/Ranked-List-Loss-for-DML

Unbiased Evaluation of Deep Metric Learning Algorithms

https://github.com/XuLena/3D-sensing

nuScenes: A multimodal dataset for autonomous driving

https://github.com/XuanjieXiao/vehicle-detection-by-using-camera

YOLO9000: Better, Faster, Stronger

https://github.com/Xunpeng746/ACMo

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

https://github.com/YC-Lai/Sequential-DDETR

Deformable DETR: Deformable Transformers for End-to-End Object Detection

https://github.com/YCHung1998/Object-Detection

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

https://github.com/YCJGG/Muti-style-transfer-by-camera

Universal Style Transfer via Feature Transforms

https://github.com/YIZHE12/fashiontags

Focal Loss for Dense Object Detection

https://github.com/YIZHE12/robots

Focal Loss for Dense Object Detection

https://github.com/YONGQUAN-QU/CBAM.Flax

CBAM: Convolutional Block Attention Module

https://github.com/YPCC/medical-data

Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

https://github.com/Yale-LILY/LectureBank

CLICKER: A Computational LInguistics Classification Scheme for Educational Resources

https://github.com/Yamahitsuji/darknet

YOLOv3: An Incremental Improvement

https://github.com/Yan107351111/SSOD

Learning to Detect and Retrieve Objects from Unlabeled Videos

https://github.com/Yang-Q-D/mindsporeCode/tree/master/ibnnet

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

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