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Pedestrian Attribute Recognition
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Pedestrian attribution recognition is the task of recognizing pedestrian features - such as whether they are talking on a phone, whether they have a backpack, and so on.
<span style="color:grey; opacity: 0.6">( Image credit: HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cocopambag/ResNet
https://github.com/code-implementation1/Code1/tree/main/densenet
https://github.com/code-implementation1/Code3/tree/main/FaceAttribute
https://github.com/code-implementation1/Code5/tree/main/lresnet100e_ir
https://github.com/code-implementation1/Code7/tree/main/resnetv2
https://github.com/cornell-zhang/allo-pldi24-artifact
https://github.com/danielamassiceti/CCA-visualdialogue
https://github.com/danielparada1/RL_SpaceInvaders
https://github.com/datoboat/Vehicle-Detection
https://github.com/dead-mazai/avia-missing-airplanes
https://github.com/deepmind/dm-haiku
https://github.com/deepmodeling/deepmd-kit
https://github.com/deepquestai/fire-smoke-dataset
https://github.com/demul/ResidualNet
https://github.com/deontaepharr/Residual-Attention-Network
https://github.com/dmizr/phuber
https://github.com/dnddnjs/pytorch-cifar10
https://github.com/dssbxx/k_labels_resnet50
https://github.com/duchaba/Norwegian_Blue_Parrot_k2fa_AI
https://github.com/earthLD/Algorithm
https://github.com/enzomuschik/distilfnd
https://github.com/eric-erki/IdenProf
https://github.com/ericjang/odin
https://github.com/evantkchong/LEPAR
Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method
https://github.com/ewinata/CIFAR-10
https://github.com/fL0n9/SKFAC-MindSpore
https://github.com/fabiofumarola/ultrayolo
https://github.com/fabxy/accessibilityCV
https://github.com/facebook/fb.resnet.torch
https://github.com/facebookarchive/fb.resnet.torch
https://github.com/facebookresearch/DomainBed
https://github.com/facebookresearch/hiera
https://github.com/facebookresearch/pycls
https://github.com/facebookresearch/stochastic_gradient_push
https://github.com/facundoq/transformational_measures_experiments
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.