Papers with Code 아카이브
Crowd Counting
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1
https://github.com/3P2S/arcface
https://github.com/AirBernard/DenseNet-by-Pytorch
https://github.com/Anderies/stochastic-constant-pruninglayers
https://github.com/AngusG/bn-advex-zhang-fixup
https://github.com/Anil1331/DenseNet
https://github.com/ApexPredator1/DenseNet_tensorflow
https://github.com/ArkaJU/SegNet---Chromosome
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/Asthestarsfalll/DenseNet-MegEngine
https://github.com/AyushAniket/ResNET-and-various-normalizations
https://github.com/BIGKnight/ADCrowd_pytorch_implementation
ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding
https://github.com/Baichenjia/Resnet
https://github.com/Bao-Jiarong/ResNet
https://github.com/Bazingaliu/learning_CSRNet
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/Burf/ResNet-Tensorflow2
https://github.com/ByakuyaUncia/DenseNetUncia
https://github.com/CVxTz/face_age_gender
https://github.com/CanshangD/ResNet_tensorflow2.0
https://github.com/CeLaMbDa/DenseNet_keras
https://github.com/CellSMB/EM-net
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models
https://github.com/Charmve/Semantic-Segmentation-PyTorch/blob/master/models/seg_net.py
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/Ciprian95/Licenta
https://github.com/Cjl-MedSeg/DenseNet
https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest
https://github.com/CodeChefVIT/VOID
https://github.com/CommissarMa/CSRNet-pytorch
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/CommissarMa/Context-Aware_Crowd_Counting-pytorch
https://github.com/CommissarMa/MCNN-pytorch
https://github.com/ConstantSun/CSR_KD
https://github.com/Contamination-Classification/DenseNet
https://github.com/CorneliusHsiao/FoodMethodGAN
https://github.com/CrossEntropy/Gesture-images-recognition-ResidualNetwork
https://github.com/CryptoSalamander/pytorch_paper_implementation
https://github.com/D-X-Y/ResNeXt-DenseNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.