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Crowd Counting

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평가표: 13
보존된 평가 기록: 155

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.

Source: Deep Density-aware Count Regressor

코드

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

https://github.com/DableUTeeF/keras-efficientnet

Deep Residual Learning for Image Recognition

https://github.com/DaikiTanak/manifold_mixup

Deep Residual Learning for Image Recognition

https://github.com/DarkGeekMS/Semantic_Segmentation_Models_Keras

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

https://github.com/Deci-AI/super-gradients

Deep Residual Learning for Image Recognition

https://github.com/DeepMark/deepmark

Deep Residual Learning for Image Recognition

https://github.com/DeepTrial/Retina-VesselNet

Densely Connected Convolutional Networks

https://github.com/DiaoXY/CSRnet

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

https://github.com/DogeZen/resnet2onnx2tensorrt

Deep Residual Learning for Image Recognition

https://github.com/Duplums/bhb10k-dl-benchmark

Deep Residual Learning for Image Recognition

https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation

Deep Residual Learning for Image Recognition

https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation

Deep Residual Learning for Image Recognition

https://github.com/Elvictor01/Plant-Pathology-2020---FGVC7-Identify-the-category-of-foliar-diseases-in-apple-trees

Densely Connected Convolutional Networks

https://github.com/EricAlcaide/MiniFold

Deep Residual Learning for Image Recognition

https://github.com/EstherBear/implementation-of-pruning-filters

Deep Residual Learning for Image Recognition

https://github.com/Fangrn/caffe-segnet

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

https://github.com/FrancescoSaverioZuppichini/ResNet

Deep Residual Learning for Image Recognition

https://github.com/GKalliatakis/Keras-VGG16-places365

Deep Residual Learning for Image Recognition

https://github.com/GSNCodes/Breast_Cancer_Detection_CNN

Densely Connected Convolutional Networks

https://github.com/GeoTrouvetout/Vehicle_ReID

Densely Connected Convolutional Networks

https://github.com/Glp91/fire-detection

Deep Residual Learning for Image Recognition

https://github.com/Graylab/deepH3-distances-orientations

Deep Residual Learning for Image Recognition

https://github.com/HAN-ARK/GVSS-S.A.Drone

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

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

Deep Residual Learning for Image Recognition

https://github.com/HLinShan/vision_networks

Densely Connected Convolutional Networks

https://github.com/Halesu/4th-ML100Days

Deep Residual Learning for Image Recognition

https://github.com/HaoyueBaiZJU/SACANet-VisDrone-Crowd

Crowd Counting on Images with Scale Variation and Isolated Clusters

https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning

Deep Residual Learning for Image Recognition

https://github.com/Harsharma2308/PoseRefinement

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

https://github.com/Hashir44/muraxray

Densely Connected Convolutional Networks

https://github.com/HuynhKEn/Variations-of-SFANet-for-Crowd-Counting

Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting

https://github.com/IBM/NeuronAlignment

Deep Residual Learning for Image Recognition

https://github.com/IMvision12/keras-vision-models

Deep Residual Learning for Image Recognition

https://github.com/JContro/pytorch_projects

Deep Residual Learning for Image Recognition

https://github.com/JHLee0513/Salt_detection_challenge

Deep Residual Learning for Image Recognition

https://github.com/JONGSKY/paper

Deep Residual Learning for Image Recognition

https://github.com/JTan2231/DenseNet

Densely Connected Convolutional Networks

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