Papers with Code 아카이브

Object Detection

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전체 기법전체 데이터셋

하위 태스크

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평가표

124

보존된 평가 기록

977

코드

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

https://github.com/spencerkraisler/Finger-Counter

SSD: Single Shot MultiBox Detector

https://github.com/spiideo/sskit

https://github.com/spirka3/master-cnn

Where and What: Driver Attention-based Object Detection

https://github.com/sprenkle/VectorCards

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

https://github.com/squirrelim/sr-frcnn

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/sraashis/ature

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sraashis/deepdyn

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sremes/a2d2

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/srihari-humbarwadi/YOLOv1-TensorFlow2.0

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

https://github.com/srihari-humbarwadi/retinanet-tensorflow2.x

Focal Loss for Dense Object Detection

https://github.com/srihari-humbarwadi/tensorflow_fcos

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/srihari-humbarwadi/tensorflow_retinanet

Focal Loss for Dense Object Detection

https://github.com/sriluk9/MalariaCells-ObjectDetection-Using-FasterRCNN

Applying Faster R-CNN for Object Detection on Malaria Images

https://github.com/srinu6/Stereo-3D-Object-Detection-for-Autonomous-Driving

Stereo R-CNN based 3D Object Detection for Autonomous Driving

https://github.com/sryab2001/Yolo-CG

YOLOv3: An Incremental Improvement

https://github.com/sryab2001/aquasaiclone

YOLOv3: An Incremental Improvement

https://github.com/ssanyachetwani/Fashion-Trend-Detection-and-Recommendation-Model

Mask R-CNN

https://github.com/ssaru/You_Only_Look_Once

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

https://github.com/sserg-r/srgan

Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks

https://github.com/sshaoshuai/PV-RCNN

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

https://github.com/sshaoshuai/Pointnet2.PyTorch

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

https://github.com/stanford-cs348k/asst2-mobilenet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/stanlee321/LightFlow-Keras

Towards High Performance Video Object Detection for Mobiles

https://github.com/stanlee321/LightFlow-TensorFlow

Towards High Performance Video Object Detection for Mobiles

https://github.com/stanleycelestin1/AirsimDetectron

Mask R-CNN

https://github.com/stanleyjzheng/PyData

Rethinking Pre-training and Self-training

https://github.com/starfdu1418/scope

Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception

https://github.com/starimpact/arm_SNIPER

SNIPER: Efficient Multi-Scale Training

https://github.com/starman360/openpose_xavier

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

https://github.com/stdbreaks/centernet

Objects as Points

https://github.com/stdereka/knowledge-distillation

An Embarrassingly Simple Approach for Knowledge Distillation

https://github.com/steph1793/Voxelnet

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

https://github.com/stephenharris/yolo-walkthrough

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

https://github.com/stevedepp/yolo4asl

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/steven-lang/arxiv-downloader

SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing

https://github.com/stevensmiley1989/MoNuSAC_Grand_Challenge_S4xUNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

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