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

https://github.com/Dodant/ANPR-with-Yolov4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/DogeZen/resnet2onnx2tensorrt

Deep Residual Learning for Image Recognition

https://github.com/DominickZhang/Distillation-Swin-Transformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/DonCorle0ne/damage_detection

YOLO9000: Better, Faster, Stronger

https://github.com/Dong1P/MTRNN

Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training

https://github.com/Donvink/Car_Detection_for_Autonomous_Driving

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

https://github.com/DoranLyong/SPANet-official

Spectral-Adaptive Modulation Networks for Visual Perception

https://github.com/Doyosae/Deep-Complex-Networks

On Complex Valued Convolutional Neural Networks

https://github.com/Doyosae/Deep_Complex_Networks

On Complex Valued Convolutional Neural Networks

https://github.com/Doyosae/Stand-Alone-Self-Attention

Stand-Alone Self-Attention in Vision Models

https://github.com/DrMMZ/RetinaNet

Focal Loss for Dense Object Detection

https://github.com/Dragoniss/mindspore-phase2-Deep-Fourier-Upsampling/blob/main/Fourier-Upsampling.py

Deep Fourier Up-Sampling

https://github.com/DuaneNielsen/keypoints

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/DucQuan12/Classification-with-activation-mish

Mish: A Self Regularized Non-Monotonic Activation Function

https://github.com/Duplums/bhb10k-dl-benchmark

Deep Residual Learning for Image Recognition

https://github.com/DwightFoster/Pytorch-TecoGan

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/Dycollapsar/Attention-Based-for-Medicalimaging

Squeeze-and-Excitation Networks

https://github.com/E-DEEP/PapersReview

Zero-Shot Instance Segmentation

https://github.com/EMI-Group/FaPN

Feature Pyramid Networks for Object Detection

https://github.com/EMI-Group/TransferLearning-Tasks

RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning

https://github.com/EMalagoli92/GCViT-TensorFlow

Global Context Vision Transformers

https://github.com/EMalagoli92/VAN-Classification-TensorFlow

Visual Attention Network

https://github.com/EMsnap/RobotSorting

YOLO9000: Better, Faster, Stronger

https://github.com/ENSTA-U2IS/MUAD-Dataset

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

https://github.com/EPFL-VILAB/3DCommonCorruptions

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/EPFL-VILAB/XDEnsembles

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/EbadSyed/spadRGBD

Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional Networks

https://github.com/Ededu1984/car_detection_forza_horizon

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation

Deep Residual Learning for Image Recognition

https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation

Deep Residual Learning for Image Recognition

https://github.com/Edmonton-School-of-AI/ml5-Simple-Image-Classification

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/EdwardTyantov/ultrasound-nerve-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Edwardwaw/gfocalv2

Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection

https://github.com/EdwinAlegria/object_semantic_deeplabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/EhudBarnea/ContextAnalysis

Exploring the Bounds of the Utility of Context for Object Detection

https://github.com/EliPassov/classification-ensembles

Data Distillation: Towards Omni-Supervised Learning

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