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/MS-Mind/MS-Code-02/tree/main/configs/resnet
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnext
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/rexnet
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/senet
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/shufflenet_v1
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/sknet
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/squeezenet
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/swintransformer
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/visformer
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/vit
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/xcit
https://github.com/MS-Mind/MS-Code-06/tree/main/NFNet
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/MS-Mind/MS-Code-06/tree/main/PSPNet
https://github.com/MS-Mind/MS-Code-06/tree/main/ntsnet
https://github.com/MS-Mind/MS-Code-06/tree/main/pointtransformer
https://github.com/MS-Mind/MS-Code-06/tree/main/proxylessnas
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/MS-Mind/MS-Code-07
https://github.com/MS-Mind/MS-Code-08/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/MS-Mind/MS-Code-08/tree/main/mobilenetV3_small_x1_0
https://github.com/MS-Mind/MS-Code-08/tree/main/mobilenetv3_large
https://github.com/MS-P3/code3/tree/main/layoutlm
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/MS-P3/code3/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/MS-P3/code3/tree/main/lilt
https://github.com/MS-P3/code4/tree/main/SLAB
https://github.com/MS-P3/code7/tree/main/vit
ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond
https://github.com/MS-P3/code7/tree/main/vivit
https://github.com/MS-P3/code7/tree/main/xlm_roberta_xl
https://github.com/MTG/music-ner
https://github.com/Machine-Learning-Tokyo/Poetry-GAN
https://github.com/MachineLearningJournalClub/LearningNLP
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/MacroMayhem/OnMixup
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
https://github.com/MacroMayhem/SharpenFocus-Pytorch
Sharpen Focus: Learning with Attention Separability and Consistency
https://github.com/MaczekO/AttentionNetworkProject
https://github.com/MagnusCaligo/EvoCNN
Evolving Deep Convolutional Neural Networks for Image Classification
https://github.com/Mahdidrm/Emotion-Recognition
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.