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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/MindSpore-paper-code-2/code3/tree/main/ssd_resnet34
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MioChiu/ResNet_TensorFlow
https://github.com/MishaLaskin/vq-vae
https://github.com/MishaLaskin/vqvae
https://github.com/Mnpr/OC-DeepLearning
https://github.com/Mnpr/Thoractic-Diseases-Detection
https://github.com/Mnpr/Thoractic_Disease_Detection
https://github.com/MohamedAliRashad/Crowd-DCNet
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer
https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
https://github.com/NASA-NeMO-Net/NeMO-Net
https://github.com/NVIDIA/retinanet-examples
https://github.com/Nathangitlab/Backdoor-Attacks-on-Crowd-Counting
https://github.com/Neerajj9/CSRNet-keras
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/NeuronDroid/GVSS-S.A.Drone
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/Nonad/Twins-with-RFB
CCTrans: Simplifying and Improving Crowd Counting with Transformer
https://github.com/OlafenwaMoses/Action-Net
https://github.com/OlafenwaMoses/IdenProf
https://github.com/OlafenwaMoses/Traffic-Net
https://github.com/Olayemiy/Image-Classification-With-Resnet
https://github.com/PPPrior/i3d-pytorch
https://github.com/PRBonn/bonnet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/PaddlePaddle/PaddleClas
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleSeg
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/Paultool/segnet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/RQuispeC/pytorch-ACSCP
https://github.com/RRoundTable/OceanLitter_dataset_generator
https://github.com/RTalha/CROWD-COUNTING-USING-CSRNET
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/RaghavHub/rekuten_hack
https://github.com/RajdeepBiswas/Manufacturing-Quality-Inspection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.