Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

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

https://github.com/Thehunk1206/Covid-19-covidcnn

Deep Residual Learning for Image Recognition

https://github.com/Thehunk1206/PRANet-Polyps-Segmentation

PraNet: Parallel Reverse Attention Network for Polyp Segmentation

https://github.com/Theopetitjean/DenseFusion_R_Invariant

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

https://github.com/ThiagoPiovesan/PeopleCounter_module

SSD: Single Shot MultiBox Detector

https://github.com/ThomasEhling/Weight_Standardization

Micro-Batch Training with Batch-Channel Normalization and Weight Standardization

https://github.com/TiagoCortinhal/SalsaNext

Deep Learning for 3D Point Clouds: A Survey

https://github.com/TiantianWang/ICCV17_SRM

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

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/TianyiZhang0315/CV_FinalProject

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/TimVerion/caffe_rfcn

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

https://github.com/Tirth27/Skin-Cancer-Classification-using-Deep-Learning

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/TobiasGruening/ARU-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Tony363/Teamplay_Yolov3

YOLOv3: An Incremental Improvement

https://github.com/Tony607/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/TonyRays/U-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/TonyYaoMSU/ProjectedGradientDescent

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/TooTouch/WhiteBox-Part1

CBAM: Convolutional Block Attention Module

https://github.com/Tota-Nakatani/SSD_keras

SSD: Single Shot MultiBox Detector

https://github.com/TotoHugo/facedetection-lite-Outsystems

Object Detection with Pixel Intensity Comparisons Organized in Decision Trees

https://github.com/Totraproducts/AerialObjectDetection

Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

https://github.com/TowardHumanizedInteraction/TripletFace

Deep Residual Learning for Image Recognition

https://github.com/Traffic-X/MonoUNI

https://github.com/Traffic-X/ViT-CoMer

https://github.com/TranThienDat-Nguyen/VisionEvaluation

How Trustworthy are Performance Evaluations for Basic Vision Tasks?

https://github.com/TravisLeeTS/grabcvchallenge

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/TravisTorch/Pytorch

YOLOv3: An Incremental Improvement

https://github.com/TristansCloud/YellowstonesVegitiation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Tsejing/object_detection

SSD: Single Shot MultiBox Detector

https://github.com/Tu-kun/cultivated_landdivision

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/TuSimple/RangeDet

Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection

https://github.com/TuSimple/mx-maskrcnn

Mask R-CNN

https://github.com/TuSimple/neuron-selectivity-transfer

Like What You Like: Knowledge Distill via Neuron Selectivity Transfer

https://github.com/TugberkErol/Semantic-Segmentation-/blob/main/unet.py

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/TurtleGo/project-vehicle-detect

SSD: Single Shot MultiBox Detector

https://github.com/TusharMhaske28/Object-Detection

YOLOv3: An Incremental Improvement

https://github.com/TusharMhaske28/Thermal-Object

YOLOv3: An Incremental Improvement

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