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코드

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

https://github.com/johnnylu305/resnet-50-101-152

Deep Residual Learning for Image Recognition

https://github.com/johntd54/stanford_car

Densely Connected Convolutional Networks

https://github.com/jonnor/datascience-master

Densely Connected Convolutional Networks

https://github.com/jplumail/people-counting

A Real-Time Deep Network for Crowd Counting

https://github.com/jqueguiner/camembert-as-a-service

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/jqueguiner/image-segmentation

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/jtcass01/SIMON

Deep Residual Learning for Image Recognition

https://github.com/julik43/IIMAS-USCS

Deep Residual Learning for Image Recognition

https://github.com/jung-jun-uk/mixface

Deep Residual Learning for Image Recognition

https://github.com/jvoynow/ConvNet-Architectures

Deep Residual Learning for Image Recognition

https://github.com/k-miran/hear

Deep Residual Learning for Image Recognition

https://github.com/kamanphoebe/motiondetection

Deep Residual Learning for Image Recognition

https://github.com/karanjsingh/Improved-CSRNet

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

https://github.com/karanjsingh/S-DCNet_CrowdCount

From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

https://github.com/kaseris/ILSVRCPlus

Deep Residual Learning for Image Recognition

https://github.com/kclip/localized-adaptive-risk-control

Deep Residual Learning for Image Recognition

https://github.com/keras-team/keras-applications

Deep Residual Learning for Image Recognition

https://github.com/kingcong/densenet

Densely Connected Convolutional Networks

https://github.com/kingcong/gpu_FaceAttribute

Deep Residual Learning for Image Recognition

https://github.com/kingcong/gpu_SE-Net

Deep Residual Learning for Image Recognition

https://github.com/kingcong/resnet

Deep Residual Learning for Image Recognition

https://github.com/kiranu1024/deep-learning

Deep Residual Learning for Image Recognition

https://github.com/kishanAk21/Skin-Cancer-Image-Classification

Deep Residual Learning for Image Recognition

https://github.com/kishorkuttan/traffic_net

Deep Residual Learning for Image Recognition

https://github.com/km1414/CNN-models

Deep Residual Learning for Image Recognition

https://github.com/kmzzhang/nbi

Deep Residual Learning for Image Recognition

https://github.com/konstantinos-p/image_classification_SOTA

Deep Residual Learning for Image Recognition

https://github.com/koshian2/ResNet-MultipleFramework

Deep Residual Learning for Image Recognition

https://github.com/krishnakarthi/COVID-19_Prediction

Deep Residual Learning for Image Recognition

https://github.com/krutikabapat/Crowd_Counting

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

https://github.com/kulkarnikeerti/SegNet-Semantic-Segmentation

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/kun-woo-park/Deeplearning_project_STL_10

Deep Residual Learning for Image Recognition

https://github.com/kwantommy/fgvc6-kaggle-cassava-classification

Deep Residual Learning for Image Recognition

https://github.com/kwotsin/TensorFlow-ENet

Deep Residual Learning for Image Recognition

https://github.com/kyle-dorman/bayesian-neural-network-blogpost

Deep Residual Learning for Image Recognition

https://github.com/lab-midas/med_segmentation

Deep Residual Learning for Image Recognition

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