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Crowd Counting
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Crowd Counting is a task to count people in image. It is mainly used in real-life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jonnor/datascience-master
https://github.com/jplumail/people-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
https://github.com/julik43/IIMAS-USCS
https://github.com/jung-jun-uk/mixface
https://github.com/jvoynow/ConvNet-Architectures
https://github.com/k-miran/hear
https://github.com/kamanphoebe/motiondetection
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
https://github.com/kclip/localized-adaptive-risk-control
https://github.com/keras-team/keras-applications
https://github.com/kingcong/densenet
https://github.com/kingcong/gpu_FaceAttribute
https://github.com/kingcong/gpu_SE-Net
https://github.com/kingcong/resnet
https://github.com/kiranu1024/deep-learning
https://github.com/kishanAk21/Skin-Cancer-Image-Classification
https://github.com/kishorkuttan/traffic_net
https://github.com/km1414/CNN-models
https://github.com/kmzzhang/nbi
https://github.com/konstantinos-p/image_classification_SOTA
https://github.com/koshian2/ResNet-MultipleFramework
https://github.com/krishnakarthi/COVID-19_Prediction
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
https://github.com/kwantommy/fgvc6-kaggle-cassava-classification
https://github.com/kwotsin/TensorFlow-ENet
https://github.com/kyle-dorman/bayesian-neural-network-blogpost
https://github.com/lab-midas/med_segmentation
https://github.com/labmlai/annotated_deep_learning_paper_implementations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.