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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/lcylmhlcy/ResnetCrowd-Caffe

ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification

https://github.com/leemathew1998/GradientWeight

Deep Residual Learning for Image Recognition

https://github.com/leemathew1998/RG

Deep Residual Learning for Image Recognition

https://github.com/leeyeehoo/CSRNet-pytorch

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

https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION

Deep Residual Learning for Image Recognition

https://github.com/lionelmessi6410/ntga

Densely Connected Convolutional Networks

https://github.com/liuyao12/ConvNets-PDE-perspective

Deep Residual Learning for Image Recognition

https://github.com/liuzhuang13/DenseNetCaffe

Densely Connected Convolutional Networks

https://github.com/lixihan/Resnet

Deep Residual Learning for Image Recognition

https://github.com/lixixibj/forecasting-with-time-series-imaging

Deep Residual Learning for Image Recognition

https://github.com/lixixibj/tsImage-forecasting

Deep Residual Learning for Image Recognition

https://github.com/ljy-hy/mentormix_pytorch

Deep Residual Learning for Image Recognition

https://github.com/lkeonwoo94/DL_cv-mdt_NVIDIA_Cert_Course_StudyPI

Deep Residual Learning for Image Recognition

https://github.com/lpirola13/flower-recognizer

Densely Connected Convolutional Networks

https://github.com/lpirola13/flower_recognizer

Densely Connected Convolutional Networks

https://github.com/ls-da3m0ns/Short-ResNet

Deep Residual Learning for Image Recognition

https://github.com/lxy000719/ResNet-50-pneumonia-classification

Deep Residual Learning for Image Recognition

https://github.com/lyqcom/BiRealNet

CNN-based Density Estimation and Crowd Counting: A Survey

https://github.com/mabhay3420/Deep-Into-CNN

Deep Residual Learning for Image Recognition

https://github.com/maciejdomagala/DLWP_notes

Deep Residual Learning for Image Recognition

https://github.com/magnificent1208/r3det-on-mmdetection

Deep Residual Learning for Image Recognition

https://github.com/mahmoodlab/hest

Deep Residual Learning for Image Recognition

https://github.com/makatx/YOLO_ResNet

Deep Residual Learning for Image Recognition

https://github.com/malin9402/2020-0221

Deep Residual Learning for Image Recognition

https://github.com/manoranjan03/PaperImplementations

Deep Residual Learning for Image Recognition

https://github.com/marload/ConvNets-TensorFlow2

Deep Residual Learning for Image Recognition

https://github.com/masoudnick/brain-tumor-mri-classification

Deep Residual Learning for Image Recognition

https://github.com/matakshay/Neural_Image_Caption_Generator

Deep Residual Learning for Image Recognition

https://github.com/maxwelltsai/DeepGalaxy

Deep Residual Learning for Image Recognition

https://github.com/mbsariyildiz/resnet-pytorch

Deep Residual Learning for Image Recognition

https://github.com/mdda/deep-learning-models

Deep Residual Learning for Image Recognition

https://github.com/megvii-research/basecls/tree/main/zoo/public/resnet

Deep Residual Learning for Image Recognition

https://github.com/mhagiwara/nanigonet

Deep Residual Learning for Image Recognition

https://github.com/microsoft/fb.resnet.torch

Deep Residual Learning for Image Recognition

https://github.com/midusi/cacic2019-handshapes

Densely Connected Convolutional Networks

https://github.com/mindlab-ai/mindcv/blob/main/examples/train_cifar_dynamic.py

Deep Residual Learning for Image Recognition

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