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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/GuessWhatGame/guesswhat

GuessWhat?! Visual object discovery through multi-modal dialogue

https://github.com/Guillem96/efficient-net-tf

Focal Loss for Dense Object Detection

https://github.com/Guillem96/efficientdet-tf

Focal Loss for Dense Object Detection

https://github.com/Guillem96/ssd-pytorch

SSD: Single Shot MultiBox Detector

https://github.com/GuoLiuFang/maskrcnn-benchmark-lfs

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/GuoQuanhao/EfficientDet-Paddle

EfficientDet: Scalable and Efficient Object Detection

https://github.com/GuoQuanhao/YOLOv4-Paddle

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Gus-Guo/AugFPN

AugFPN: Improving Multi-scale Feature Learning for Object Detection

https://github.com/HCPLab-SYSU/KERN

Neural Motifs: Scene Graph Parsing with Global Context

https://github.com/HHousen/object-discovery-pytorch

Object-Centric Learning with Slot Attention

https://github.com/HRNet/HRNet-FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/HSRW-EMRP-WS1920-Drone/object_detection_and_landing

YOLO9000: Better, Faster, Stronger

https://github.com/HaipengXiong/weighted-hausdorff-loss

Locating Objects Without Bounding Boxes

https://github.com/HaiqalLM10/YOLO-Vehicle-Counting

YOLO9000: Better, Faster, Stronger

https://github.com/HaojunYuPKU/MOT_detectron

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

https://github.com/HardLaugh/EfficientDet-bifpn

EfficientDet: Scalable and Efficient Object Detection

https://github.com/Hardly-Human/Yolo-Object-Detector

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Harut0726/my-pointnet-tensorflow

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

https://github.com/Harut0726/votenet

Deep Hough Voting for 3D Object Detection in Point Clouds

https://github.com/Hayashi-Yudai/ML_models

Adversarial Complementary Learning for Weakly Supervised Object Localization

https://github.com/He-Zhenwei/MAF

Multi-adversarial Faster-RCNN for Unrestricted Object Detection

https://github.com/Healthcare-Robotics/smm50

Classification of Household Materials via Spectroscopy

https://github.com/Heedeok/frustum-pointnets-rgbd

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

https://github.com/HeegonJin/yolov1

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Hernandope/keras_resnet_FPN_cifar10

Feature Pyramid Networks for Object Detection

https://github.com/HiKapok/SSD.TensorFlow

SSD: Single Shot MultiBox Detector

https://github.com/HiLab-git/DTC

Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

https://github.com/HirataYurina/yoloV3-keras-sibyl

Weighted boxes fusion: Ensembling boxes from different object detection models

https://github.com/HirataYurina/yoloV4-keras-techi

Weighted boxes fusion: Ensembling boxes from different object detection models

https://github.com/HirataYurina/yolov4-tiny-keras

Weighted boxes fusion: Ensembling boxes from different object detection models

https://github.com/HoganZhang/FairMOT

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

https://github.com/HoganZhang/mot_neural_solver

Tracking without bells and whistles

https://github.com/HongSic/DarknetAI

YOLO9000: Better, Faster, Stronger

https://github.com/HungryCookie/VisDrone_CenterNet

Objects as Points

https://github.com/HusterRC/adversarial-frcnn-master

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

https://github.com/HuynhThanhQuan/skin-detective

Microsoft COCO: Common Objects in Context

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