Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_inceptionv2
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_resnet50
https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/mae
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/swin
https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_06_convolutional-neural-networks
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_07_convolutional-modern
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/2023-MindSpore-4/Code13/tree/main/zhangchangqing/TMC_MS-main
https://github.com/2023-MindSpore-4/Code14/tree/main/ResNeSt50
https://github.com/2023-MindSpore-4/Code14/tree/main/ResNeXt
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/2023-MindSpore-4/Code14/tree/main/ResidualAttentionNet
https://github.com/2023-MindSpore-4/Code14/tree/main/S-GhostNet
https://github.com/2023-MindSpore-4/Code14/tree/main/SE_ResNeXt50
https://github.com/2023-MindSpore-4/Code14/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/res2net
https://github.com/2023-MindSpore-4/Code14/tree/main/resnet50_adv_pruning
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/2023-MindSpore-4/Code14/tree/main/resnetv2_50_frn
https://github.com/2023-MindSpore-4/Code14/tree/main/resnext152_64x4d
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet101
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet152
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/simclr
https://github.com/2023-MindSpore-4/Code2/tree/main/dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/2023-MindSpore-4/Code3/tree/main/densenet
https://github.com/2023-MindSpore-4/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-4/Code4/tree/main/ProC-KD-main
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style
https://github.com/2023-MindSpore-4/Code6/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-4/Code6/tree/main/ResNeSt50
https://github.com/2023-MindSpore-4/Code6/tree/main/ResidualAttentionNet
https://github.com/2023-MindSpore-4/Code7/tree/main/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/2023-MindSpore-4/Code7/tree/main/sknet
https://github.com/2023-MindSpore-4/Code7/tree/main/sr_ea
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
https://github.com/2023-MindSpore-4/Code7/tree/main/ssc_resnet50
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_inceptionv2
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.