Papers with Code 아카이브

2D Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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보존된 평가 기록

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코드

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

https://github.com/Jaekyu-Sim/Open_Pose_for_Naver

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

https://github.com/JagadishSivakumar/GlobalWheatDetection

EfficientDet: Scalable and Efficient Object Detection

https://github.com/JagadishSivakumar/Image-Classifier-ml5.js

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/JaidedAI/EasyOCR

Deep Residual Learning for Image Recognition

https://github.com/Jakaria08/EESRGAN

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

https://github.com/Jakaria08/F-EESRGAN

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

https://github.com/JakobGroeftehauge/BachelorProject

Objects as Points

https://github.com/JakobGroeftehauge/Brick-Pose-Estimation

Objects as Points

https://github.com/JakobKallestad/InceptionV3-on-plankton-images

Going Deeper with Convolutions

https://github.com/James-Gilbert-/medical-deep-learning

Deep Residual Learning for Image Recognition

https://github.com/James-Gilbert-/pneumonia-detection

Deep Residual Learning for Image Recognition

https://github.com/JamesLiao714/MC_GAN

Multi-Content GAN for Few-Shot Font Style Transfer

https://github.com/Jamie725/Multimodal-Object-Detection-via-Probabilistic-Ensembling

Multimodal Object Detection via Probabilistic Ensembling

https://github.com/JanMarcelKezmann/Residual-Network-Architectures

Deep Residual Learning for Image Recognition

https://github.com/JanMarcelKezmann/TensorFlow-Advanced-Segmentation-Models

Pyramid Scene Parsing Network

https://github.com/JanOPedersen/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Jangho-Kim/PSG-pytorch

Data-Free Quantization Through Weight Equalization and Bias Correction

https://github.com/Janghyeonwoong/Random-Erasing-Tensorflow2

Random Erasing Data Augmentation

https://github.com/Jarr0d/Human-Parsing-Network

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/Jarvis73/Group_Normalization

Group Normalization

https://github.com/JaryHuang/awesome_SSD_FPN_GIoU

SSD: Single Shot MultiBox Detector

https://github.com/JaryV/CycleGAN_OldYoung

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/JasonLiTW/simple-railway-captcha-solver

Going Deeper with Convolutions

https://github.com/Jasonkks/mlcnet

Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency

https://github.com/Jasonlee1995/AutoAugment_Detection

Learning Data Augmentation Strategies for Object Detection

https://github.com/JasperNg/ColorPreservationYUV

Preserving Color in Neural Artistic Style Transfer

https://github.com/Jastot/Rodinka_Neural_Network

Going Deeper with Convolutions

https://github.com/JavierAntoran/pictocatcher

Going Deeper with Convolutions

https://github.com/Jay0505/CycleGAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/JayPatwardhan/ResNet-PyTorch

Deep Residual Learning for Image Recognition

https://github.com/Jayfeather1024/Backdoor-Enhanced-Alignment

Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment

https://github.com/Jeffkang-94/pytorch-adversarial-attack

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/JeffreyCA/spleeterweb-xumx

All for One and One for All: Improving Music Separation by Bridging Networks

https://github.com/JeffreyCA/xumx

All for One and One for All: Improving Music Separation by Bridging Networks

https://github.com/JennEYoon/Coursera-DLAI

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

https://github.com/JennyVanessa/Paddle-GHM

Gradient Harmonized Single-stage Detector

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