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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/aadhithya/AdaIN-pytorch

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

https://github.com/aakashjhawar/traffic-sign-detection

YOLO9000: Better, Faster, Stronger

https://github.com/aakiraotok/yowov3

TOOD: Task-aligned One-stage Object Detection

https://github.com/aaron-wang-de/ab

YOLO9000: Better, Faster, Stronger

https://github.com/aaronbae/AnaQA

Reading Wikipedia to Answer Open-Domain Questions

https://github.com/aaronfriedman6/MV3D_VoxelNet

Multi-View 3D Object Detection Network for Autonomous Driving

https://github.com/aashish2000/Parking_Management

Real-time object detection method based on improved YOLOv4-tiny

https://github.com/aasimsani/artificial_manga_panel_dataset

Object Detection for Comics using Manga109 Annotations

https://github.com/abahram77/mnistChallenge

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/abahram77/mnist_challenge

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/abaietto/neural_ode_classification

Deep Residual Learning for Image Recognition

https://github.com/abajaj945/Ship-Detection-using-Tensorflow

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

https://github.com/abcxs/building_extraction

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/abcxs/maskrcnn-contest

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

https://github.com/abcxs/polyrnn

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/abdksyed/DETR

End-to-End Object Detection with Transformers

https://github.com/abdoelsayed2016/TNCR_Dataset

Deep learning for table detection and structure recognition: A survey

https://github.com/abdoelsayed2016/tncr

Deep learning for table detection and structure recognition: A survey

https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds

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

https://github.com/abhaskumarsinha/Corpus2GPT

Language Models are Few-Shot Learners

https://github.com/abhaskumarsinha/MinimalGPT

Language Models are Few-Shot Learners

https://github.com/abhi1kumar/M3D-RPN

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

https://github.com/abhi1kumar/hrnet_pose_single_gpu

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/abhi2610/ohem

Training Region-based Object Detectors with Online Hard Example Mining

https://github.com/abhi4ssj/squeeze_and_excitation

Squeeze-and-Excitation Networks

https://github.com/abhigoku10/CenterPoint_PC

Center-based 3D Object Detection and Tracking

https://github.com/abhigoyal1997/CS-763-Project

Exploring Models and Data for Image Question Answering

https://github.com/abhijeetmittal/Convolutional-Neural-Network

A Neural Algorithm of Artistic Style

https://github.com/abhileshborode/Behavorial-Clonng-Self-driving-cars

Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues

https://github.com/abhimangalms/PlantDoctor

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/abhishek1411/event-rgb-fusion

https://github.com/abhishtagatya/paintgan

A Neural Algorithm of Artistic Style

https://github.com/abhiskk/fast-neural-style

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/abhoi/Keras-MnasNet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/abhuse/polyloss-pytorch

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/abhuse/pytorch-efficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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