https://github.com/mindspore-ai/models/tree/master/research/cv/pointnet2
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Papers with Code 아카이브
Person Re-Identification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 13
- 평가표
- 41
- 보존된 평가 기록
- 533
Person Re-Identification is a computer vision task in which the goal is to match a person's identity across different cameras or locations in a video or image sequence. It involves detecting and tracking a person and then using features such as appearance, body shape, and clothing to match their identity in different frames. The goal is to associate the same person across multiple non-overlapping camera views in a robust and efficient manner.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/MobileNetV2.py
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/mobilenet_v2.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/resnet.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/shufflenetv1.py
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/minggli/DenseNet
https://github.com/mingxingtan/efficientnet
https://github.com/mingxingtan/mnasnet
https://github.com/minhto2802/keras-shufflenet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/miraclewkf/densenet
https://github.com/miszkur/SelfSupervisedLearning
Bootstrap your own latent: A new approach to self-supervised Learning
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/mobarakol/AP-MTL
https://github.com/mobilesec/arcface-tensorflowlite
https://github.com/moddy2024/resnet-9
https://github.com/modelhub-ai/densenet
https://github.com/modelhub-ai/mobilenet
https://github.com/modelscope/modelscope
https://github.com/moelgendy/mobileNetV2
https://github.com/monkey0head/Domain_Adaptation_thesis
https://github.com/motlabs/dont-be-turtle
https://github.com/mr-bulb/DL_basic_github
https://github.com/mrkolarik/3d-brain-segmentation
https://github.com/mrzzy/np-dl-assign-1
https://github.com/msnlab/st-reid-datasets
Dissecting Person Re-identification from the Viewpoint of Viewpoint
https://github.com/mssung94/intel-image-classification
https://github.com/mszpc/osnet
Learning Generalisable Omni-Scale Representations for Person Re-Identification
https://github.com/mszpc/resnet18
https://github.com/muditrastogi/chestai
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.