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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/NYUMedML/CNN_design_for_AD
공식 코드On the design of convolutional neural networks for automatic detection of Alzheimer's disease
https://github.com/Na-Z/sess
공식 코드https://github.com/Naagar/Seeds_Classification
공식 코드Automated Seed Quality Testing System using GAN & Active Learning
https://github.com/Nandan91/ULSAM
공식 코드ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks
https://github.com/NathanUA/U-2-Net
공식 코드U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/NeurAI-Lab/NCT
공식 코드Noisy Concurrent Training for Efficient Learning under Label Noise
https://github.com/NeuralCollapseApplications/ImbalancedLearning
공식 코드https://github.com/NeuromorphicComputing/S-TEC
공식 코드https://github.com/Newbeeer/L_DMI
공식 코드https://github.com/NiWaRe/DPBenchmark
공식 코드SmoothNets: Optimizing CNN architecture design for differentially private deep learning
https://github.com/NonlinearArtificialIntelligenceLab/jaxDiversity
공식 코드Neuronal diversity can improve machine learning for physics and beyond
https://github.com/OATML/oatomobile
공식 코드Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
https://github.com/OE-FET/FraGVAE
공식 코드Fragment Graphical Variational AutoEncoding for Screening Molecules with Small Data
https://github.com/OFA-Sys/ONE-PEACE
공식 코드ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
https://github.com/Omid-Nejati/MedViT
공식 코드MedViT: A Robust Vision Transformer for Generalized Medical Image Classification
https://github.com/Omid-Nejati/MedViTV2
공식 코드Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention
https://github.com/OpenGVLab/M3I-Pretraining
공식 코드Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information
https://github.com/OpenGVLab/Vision-RWKV
공식 코드Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures
https://github.com/OpenSparseLLMs/CLIP-MoE
공식 코드CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling
https://github.com/Optimization-AI/LibAUC
공식 코드https://github.com/Orion-AI-Lab/GAIA
공식 코드GAIA: A Global, Multi-modal, Multi-scale Vision-Language Dataset for Remote Sensing Image Analysis
https://github.com/PGelss/scikit_tt
공식 코드https://github.com/PKU-ICST-MIPL/OPAM_TIP2018
공식 코드Object-Part Attention Model for Fine-grained Image Classification
https://github.com/PRIS-CV/Fine-Grained-or-Not
공식 코드https://github.com/PRIS-CV/GPCA
공식 코드GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention
https://github.com/PRIS-CV/PMG-Progressive-Multi-Granularity-Training
공식 코드Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
https://github.com/PRIS-CV/Structured-DropConnect
공식 코드Structured DropConnect for Uncertainty Inference in Image Classification
https://github.com/PROoshio/CRPM-Net
공식 코드https://github.com/PaddlePaddle/PaddleClas
공식 코드HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-layout
공식 코드ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
https://github.com/PaddlePaddle/PaddleSeg
공식 코드Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models
https://github.com/PaddlePaddle/VIMER/tree/main/StrucTexT/v2
공식 코드StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
https://github.com/PaperCodeSubmission/AAAI2021-260
공식 코드SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
https://github.com/PatrykChrabaszcz/Imagenet32_Scripts
공식 코드A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
https://github.com/PhD-TianLv/MDMLP
공식 코드MDMLP: Image Classification from Scratch on Small Datasets with MLP
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