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/MarkHershey/arxiv-dl

Deep Residual Learning for Image Recognition

https://github.com/MartinGer/Attention-Augmented-Convolutional-Networks

Attention Augmented Convolutional Networks

https://github.com/MartinGer/Bottleneck-Transformers-for-Visual-Recognition

Attention Augmented Convolutional Networks

https://github.com/MartinGer/Stand-Alone-Self-Attention-in-Vision-Models

Stand-Alone Self-Attention in Vision Models

https://github.com/Mary-xl/HRnet_Kaggle_iNat2019_FGVC

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/Masao-Taketani/FOTS_OCR

Deep Residual Learning for Image Recognition

https://github.com/Maskify/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/MasoumehVahedi/Yolo-Object-Detection

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

https://github.com/MatthewQi2002/Unet_mindspore

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Maveric4/pr19aaw01

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/MaxLikesMath/Barlow-Twins-Pytorch

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/MaximKuklin/3D_Object_Detection_Diploma

Objects as Points

https://github.com/MaximeBataille/tennis_tracking

YOLOv3: An Incremental Improvement

https://github.com/MaxwellHeller/coffee_fraud_detection

Deep Residual Learning for Image Recognition

https://github.com/MayankGupta73/Workout-Evaluation-tf-openpose

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

https://github.com/Mayurji/Image-Classification-PyTorch

Going Deeper with Convolutions

https://github.com/McDo/LightFlowPytorch

Towards High Performance Video Object Detection for Mobiles

https://github.com/MdAli1993/Multiclass_Semantic_Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/MeAmarP/Fruit-Classification

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Media-Smart/SKU110K-DenseDet

Precise Detection in Densely Packed Scenes

https://github.com/Media-Smart/vedaseg

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/MegEngine/Models/tree/master/official/vision/classification

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet

Deep Residual Learning for Image Recognition

https://github.com/MegEngine/Models/tree/master/official/vision/detection

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

https://github.com/MegEngine/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/MenGuangwen-CN-0411/Simple-Use-of-SSD

SSD: Single Shot MultiBox Detector

https://github.com/MenghaoGuo/EANet

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks

https://github.com/Meowuu7/Point-Transformer

Point Transformer

https://github.com/Mephisto405/Learning-Loss-for-Active-Learning

Learning Loss for Active Learning

https://github.com/Mersive-Technologies/yolov3

YOLOv3: An Incremental Improvement

https://github.com/MiX-S/resnet_cifar10

Deep Residual Learning for Image Recognition

https://github.com/MichaelWangfc/distributed-tensorflow-resnet

Deep Residual Learning for Image Recognition

https://github.com/Microsoft/singleshotpose

BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth

https://github.com/MikelBros/Yolov3_DeepSort_Pytorch

YOLOv3: An Incremental Improvement

https://github.com/Miltos-90/UNet_Biomedical_Image_Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Mind23-2/MindCode-1

Side-Aware Boundary Localization for More Precise Object Detection

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