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2D Classification

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코드

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

https://github.com/Guang000/Awesome-Dataset-Distillation

Backdoor Attacks Against Dataset Distillation

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/GuillaumeAI/rwml__adaptive_style_transfer

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

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/GuillermoCarbajal/NonUniformBlurKernelEstimation

Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition

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/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/GuoShi28/Caffe-Instance-Normalization-and-Guided-Instance-Normalization

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

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/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/HKUST-KnowComp/MnemonicReader

Reading Wikipedia to Answer Open-Domain Questions

https://github.com/HKUST-KnowComp/R-Net

SQuAD: 100,000+ Questions for Machine Comprehension of Text

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/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

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