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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-ai/models/tree/master/official/cv/MCNN
https://github.com/mindspore-ai/models/tree/master/official/cv/densenet
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet50_quant
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet_thor
https://github.com/mindspore-ai/models/tree/master/research/cv/FaceAttribute
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/resnet.py
https://github.com/mindspore-courses/MindSpore-classification
https://github.com/mindspore-courses/heads-on-mindspore/tree/main/1-best-practice/models
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/densenet.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/resnet.py
https://github.com/minggli/DenseNet
https://github.com/mingxingtan/efficientnet
https://github.com/miraclewkf/densenet
https://github.com/mlvccn/bmtc_transferattackvid
https://github.com/mminamina/Transfer_Learning_NaimishNet---Keypoints_Detection
https://github.com/mminamina/Transfer_Learning_ResNet18---Keypoints_Detection
https://github.com/mnguyen0226/image-augmentation-dnn-performance
https://github.com/mobilesec/arcface-tensorflowlite
https://github.com/moddy2024/resnet-9
https://github.com/modelhub-ai/densenet
https://github.com/mr-bulb/DL_basic_github
https://github.com/mrkolarik/3d-brain-segmentation
https://github.com/mrmtn86/python1
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/mrzzy/np-dl-assign-1
https://github.com/mszpc/resnet18
https://github.com/muditrastogi/chestai
https://github.com/musifahamran/FYP
https://github.com/nashawnch/Computer-Vision-FerrariDetective
https://github.com/nashawnch/ComputerVision-FerrariDetective
https://github.com/navganti/SIVO
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/navganti/SegNet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/ndb796/pytorch-adversarial-training-cifar
https://github.com/neuropoly/multiclass-segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/nguyen1312/LoViTCrowd
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.