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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/cihangxie/DAG

Adversarial Examples for Semantic Segmentation and Object Detection

https://github.com/citbrains/darknet_yolov4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/cj-mclaughlin/segmentation_research

Feature Pyramid Networks for Object Detection

https://github.com/cj4L/SSNM-Coseg

Deep Object Co-segmentation via Spatial-Semantic Network Modulation

https://github.com/cjpurackal/m2det-tf

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

https://github.com/cjvargasc/YOLOv2

YOLO9000: Better, Faster, Stronger

https://github.com/clasikman/darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/clbeggs/MobileSal

MobileSal: Extremely Efficient RGB-D Salient Object Detection

https://github.com/clemkoa/faster-rcnn

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

https://github.com/clemkoa/fasterrcnn

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

https://github.com/clive819/Modified-DETR

End-to-End Object Detection with Transformers

https://github.com/cloudnine148/PHC_2nd_SPC

YOLO9000: Better, Faster, Stronger

https://github.com/cmf3673/spotti

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/cmitash/physim-dataset-generator

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation

https://github.com/cmu-enyac/Renofeation

Efficient Object Localization Using Convolutional Networks

https://github.com/coconutzs/PointNet_zs

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

https://github.com/code-implementation1/Code1/tree/main/EfficientDet_d0

EfficientDet: Scalable and Efficient Object Detection

https://github.com/code-implementation1/Code2

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/code-implementation1/Code5/tree/main/m2det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

https://github.com/code-implementation1/Code5/tree/main/ms_rcnn

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

https://github.com/code-implementation1/Code6/tree/main/OSVOS

One-Shot Video Object Segmentation

https://github.com/code-implementation1/Code6/tree/main/PVAnet

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

https://github.com/code-implementation1/Code6/tree/main/nas-fpn

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

https://github.com/code-implementation1/Code6/tree/main/pointtransformer

Point Transformer

https://github.com/code-implementation1/Code7/tree/main/RefineDet

Single-Shot Refinement Neural Network for Object Detection

https://github.com/code-implementation1/Code7/tree/main/rcnn

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

https://github.com/code-implementation1/Code7/tree/main/res2net_faster_rcnn

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

https://github.com/code-implementation1/Code7/tree/main/rfcn

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_mobilenetv1

https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_resnet50

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

https://github.com/code-implementation1/Code9/tree/main/PointNet2

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

https://github.com/code-implementation1/Code9/tree/main/RetinaFace_ResNet50

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/code-implementation1/Code9/tree/main/SSD

SSD: Single Shot MultiBox Detector

https://github.com/code-implementation1/Code9/tree/main/ocean_model

Ocean: Object-aware Anchor-free Tracking

https://github.com/codecolony/re3-tensorflow

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

https://github.com/cokowpublic/Nuclei-Segmentation

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