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2D Classification

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

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/TurkuNLP/FinBERT

A Finnish News Corpus for Named Entity Recognition

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

https://github.com/TusharMhaske28/summer-project

YOLOv3: An Incremental Improvement

https://github.com/UBC-CIC/COVID19-L3-Net

Pyramid Scene Parsing Network

https://github.com/UESTC-Liuxin/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/UPCLJ/py-faster-rcnn

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

https://github.com/USTC-IMCL/HST-for-Compressed-SR

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/UdbhavPrasad072300/Generate-Fake-Faces-with-CVAE-in-PyTorch

Deep Feature Consistent Variational Autoencoder

https://github.com/Uemuet/style-transfer

A Style-Aware Content Loss for Real-time HD Style Transfer

https://github.com/Uemuet/styletransfer-adaptive

A Style-Aware Content Loss for Real-time HD Style Transfer

https://github.com/Ugenteraan/Masked-AutoEncoder-PyTorch

Masked Autoencoders Are Scalable Vision Learners

https://github.com/Ugenteraan/YOLOv1

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

https://github.com/Ugness/PiCANet-Implementation

PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection

https://github.com/Ultranity/Anomaly.Paddle

Towards Total Recall in Industrial Anomaly Detection

https://github.com/Unity-Technologies/SynthDet

An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection

https://github.com/UnknownUnit/Bandits_project

A Contextual-Bandit Approach to Personalized News Article Recommendation

https://github.com/UnofficialJuliaMirror/DeepMark-deepmark

Deep Residual Learning for Image Recognition

https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark

Deep Residual Learning for Image Recognition

https://github.com/Uranium-Deng/Steganalysis-StegoRemoval

CBAM: Convolutional Block Attention Module

https://github.com/UsmannK/cloud-mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/UtkarshKharche29/Automatic-Number-Plate-Recognition

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Uuuuuuho/Pillar_on_SoC

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/V3Det/Detectron2-V3Det

V3Det: Vast Vocabulary Visual Detection Dataset

https://github.com/V3Det/mmdetection-V3Det

RTMDet: An Empirical Study of Designing Real-Time Object Detectors

https://github.com/VAShibaev/text_style_transfer

Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites

https://github.com/VCIP-RGBD/DFormer

DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation

https://github.com/VCasecnikovs/Yet-Another-YOLOv4-Pytorch

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/VLTSeg/VLTSeg

Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning

https://github.com/VaanHUANG/CSCI5210HW1

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

https://github.com/VachanVY/gpt.jax

Language Models are Few-Shot Learners

https://github.com/VadimFarutin/neural-style-transfer

A Neural Algorithm of Artistic Style

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