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/MindSpore-scientific-2/code-12/tree/main/Fixup-Initialization
Fixup Initialization: Residual Learning Without Normalization
https://github.com/MindSpore-scientific-2/code-12/tree/main/GAN-master
https://github.com/MindSpore-scientific-2/code-12/tree/main/KPC-cF-main
KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA
https://github.com/MindSpore-scientific-2/code-12/tree/main/falformer
FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlm
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/MindSpore-scientific-2/code-14/tree/main/lilt
https://github.com/MindSpore-scientific-2/code-14/tree/main/m2m_100
M2m: Imbalanced Classification via Major-to-minor Translation
https://github.com/MindSpore-scientific-2/code-2/tree/main/ZoneoutRNN
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/MindSpore-scientific-2/code-3/tree/main/Three-Dimensional-Lip-Motion-Network-for-Text-Independent-Speaker-Recognition-master
https://github.com/MindSpore-scientific-2/code-3/tree/main/WaveCRN-master
Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning
https://github.com/MindSpore-scientific-2/code-3/tree/main/weakly-supervised
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations
https://github.com/MindSpore-scientific-2/code-4/tree/main/PCGrad-mindspore-example
https://github.com/MindSpore-scientific-2/code-4/tree/main/Patient2Vec-A-Personalized-Interpretable
Knowledge distillation: A good teacher is patient and consistent
https://github.com/MindSpore-scientific-2/code-4/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific-2/code-4/tree/main/rand_conv-master
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/MindSpore-scientific-2/code-5/tree/main/Datasets-Models-main
The iNaturalist Species Classification and Detection Dataset
https://github.com/MindSpore-scientific-2/code-5/tree/main/Deep-Gradient-Compression
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
https://github.com/MindSpore-scientific-2/code-5/tree/main/Last-Query-Transformer-RNN
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/MindSpore-scientific-2/code-5/tree/main/PCGrad-mindspore-example
https://github.com/MindSpore-scientific-2/code-5/tree/main/Patient2Vec-A-Personalized-Interpretable
Knowledge distillation: A good teacher is patient and consistent
https://github.com/MindSpore-scientific-2/code-5/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific-2/code-8/tree/main/PCGrad-mindspore-example
https://github.com/MindSpore-scientific-2/code-8/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific-2/code-8/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific-2/code-9/tree/main/Deep-Gradient-Compression
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
https://github.com/MindSpore-scientific-2/code-9/tree/main/Deformable_Patch_Representation
https://github.com/MindSpore-scientific/code-1/tree/main/PR_Product
PR Product: A Substitute for Inner Product in Neural Networks
https://github.com/MindSpore-scientific/code-1/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/MindSpore-scientific/code-1/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-10/tree/main/MicAugment
https://github.com/MindSpore-scientific/code-10/tree/main/PCGrad-mindspore-example
https://github.com/MindSpore-scientific/code-10/tree/main/Patient2Vec-A-Personalized-Interpretable
Knowledge distillation: A good teacher is patient and consistent
https://github.com/MindSpore-scientific/code-10/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.