Papers with Code 아카이브

Object

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Replace the cat with a British Shorthair cat of the breed with bulging yellow eyes

코드

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

https://github.com/WalterMa/gluon-faster-rcnn

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/WangMian-Maker/Flask

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Wangxy2180/darknetKinectDetect

YOLO9000: Better, Faster, Stronger

https://github.com/WangyiNTU/SCALNet

A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds

https://github.com/Wastoon/MOT-CenterNet

Weighted boxes fusion: Ensembling boxes from different object detection models

https://github.com/Wastoon/XinTong_CenterNet

Objects as Points

https://github.com/Westlake-AI/openmixup

ResizeMix: Mixing Data with Preserved Object Information and True Labels

https://github.com/Whiffe/mmaction2_YF

Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective

https://github.com/WillSuen/LostGANs

Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis

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/WongKinYiu/YOLO

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

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/Xephyrum/Agriculturice

Scalable Object Detection using Deep Neural Networks

https://github.com/Xephyrum/CropsMD

Scalable Object Detection using Deep Neural Networks

https://github.com/Xharlie/ShapenetRender_more_variation

3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction

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

SSD: Single Shot MultiBox Detector

https://github.com/Xiaohui9607/physical_interaction_video_prediction_pytorch

Unsupervised Learning for Physical Interaction through Video Prediction

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

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

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/YCHung1998/Object-Detection

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

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/YabinZhang1994/PartNet

Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network

https://github.com/Yamahitsuji/darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Yan107351111/SSOD

Learning to Detect and Retrieve Objects from Unlabeled Videos

https://github.com/Yang-YiFan/DiracDeltaNet

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/Yang2446/pointnet

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/YangtaoWANG95/TokenCut

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

https://github.com/Yashgh7076/Eluvio-ML-Scene-Segmentation

Focal Loss for Dense Object Detection

https://github.com/Yc174/frustum-pointnets

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/YiruS/pointnet_adversarial

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/Yochengliu/MLIC-KD-WSD

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

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