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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/Samyu0304/thought-propagation

Language Models are Few-Shot Learners

https://github.com/Sangkwun/Resnet

Deep Residual Learning for Image Recognition

https://github.com/SanketAgrawal/Catagorical-Focal-Loss

Focal Loss for Dense Object Detection

https://github.com/Santosh-Gupta/Arxiv-Manatee

Data-driven Summarization of Scientific Articles

https://github.com/SarderLab/WSI-cycleGAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/SarthakGarg13/OTOMYCOSIS

Deep Residual Learning for Image Recognition

https://github.com/SarthakGarg13/Severstal-Steel-Defect-Detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/SarthakYadav/audax

A ConvNet for the 2020s

https://github.com/SasCezar/XWikiRE

WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia

https://github.com/SatyamGaba/lt_od

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/SdahlSean/FilterResponseNormalizationTensorflow

Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

https://github.com/SeHwanJoo/mmdetection_vinbig

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/SebasGarcia08/sku110-dense-object-mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Seioch/darknet_v4_deploy

YOLOv3: An Incremental Improvement

https://github.com/SeniorDesignF20/AmazonUnderstandingProductImages

Spot the Difference by Object Detection

https://github.com/Sense-X/TSD

Revisiting the Sibling Head in Object Detector

https://github.com/Sentient07/HeadHunter-T

Tracking Pedestrian Heads in Dense Crowd

https://github.com/SeonbeomKim/TensorFlow-pix2pix

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/SeonghoBaek/CPC

Data-Efficient Image Recognition with Contrastive Predictive Coding

https://github.com/SeonghoBaek/CycleGAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/SeonghoBaek/FrameSequencePrediction

Data-Efficient Image Recognition with Contrastive Predictive Coding

https://github.com/SethEBaldwin/Resnet

Deep Residual Learning for Image Recognition

https://github.com/SeventhBlue/trainDarknet-yolov3

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/SeventhBlue/trainDarknet-yolov4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/SfTI-Robotics/ROS-label-node

Mask R-CNN

https://github.com/SforAiDl/CountCLIP

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/SforAiDl/vformer

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

https://github.com/Sha-Lab/FEAT

Prototypical Networks for Few-shot Learning

https://github.com/ShaharrHorn/Detection-Game-logic

YOLO9000: Better, Faster, Stronger

https://github.com/ShahedSabab/imagePrep

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ShaileshKumar97/Speech-Enhancement

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ShangHua-Gao/G2L-search

RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks

https://github.com/Shantanu48114860/MTL-TCNN3

Using Filter Banks in Convolutional Neural Networks for Texture Classification

https://github.com/ShaofengZou/A-CNN-Based-Blind-Denoising-Method

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/ShaojieJiang/tldr

Focal Loss for Dense Object Detection

https://github.com/SharadGitHub/OctaveUnet

U-Net: Convolutional Networks for Biomedical Image Segmentation

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