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

https://github.com/GregorKobsik/Octree-Transformer

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/Guanghan/CenterNet-Gluon

Objects as Points

https://github.com/Guanghan/ROLO

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

https://github.com/Guanghan/mxnet-centernet

Objects as Points

https://github.com/GuangyanZhang/SCNN-Deeplabv3-bisenet-icnet

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

https://github.com/Gugan0905/steel-defect-detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/GuillaumeDufau/Image-super-resolution-vdsr

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/Guillem96/efficient-net-tf

Focal Loss for Dense Object Detection

https://github.com/Guillem96/efficientdet-tf

Focal Loss for Dense Object Detection

https://github.com/Guillem96/ssd-pytorch

SSD: Single Shot MultiBox Detector

https://github.com/GunjanChourasia/custom_detector_yolov3

YOLOv3: An Incremental Improvement

https://github.com/GuoLiuFang/maskrcnn-benchmark-lfs

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

https://github.com/GuoQuanhao/EfficientDet-Paddle

EfficientDet: Scalable and Efficient Object Detection

https://github.com/GuoQuanhao/PRNet-Paddle

Progressive Image Deraining Networks: A Better and Simpler Baseline

https://github.com/GuoQuanhao/ScaledYOLOv4-Paddle

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/GuoQuanhao/YOLOv4-Paddle

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Gus-Guo/AugFPN

AugFPN: Improving Multi-scale Feature Learning for Object Detection

https://github.com/HAOCHENYE/yehc_mmdet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/HCPLab-SYSU/SR

Dual-Glance Model for Deciphering Social Relationships

https://github.com/HERMINDERSINGH1234/ML_Extra_Resolution_Increases

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

https://github.com/HHTseng/video-classification

Deep Residual Learning for Image Recognition

https://github.com/HHY-ZJU/Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/HO4X/TSR_JetsonTX2

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/HRNet/HRNet-FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/HRNet/HRNet-Facial-Landmark-Detection

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HRNet/HRNet-Human-Pose-Estimation

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HRNet/HRNet-Image-Classification

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HRNet/HRNet-MaskRCNN-Benchmark

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HRNet/HRNet-Object-Detection

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HRNet/HRNet-Semantic-Segmentation

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HSRW-EMRP-WS1920-Drone/object_detection_and_landing

YOLO9000: Better, Faster, Stronger

https://github.com/HSoo-Kim/SpatioTemporal-AutoEncoder

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

https://github.com/Ha0Tang/LocalGlobalGAN

Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

https://github.com/HadiZayer/UNet_pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Hadisalman/robust-verify-benchmark

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/Hadrien-Cornier/E6040-super-resolution-project

Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network

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