SOTAVerified

Image Classification

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.

Source: Metamorphic Testing for Object Detection Systems

Papers

Showing 776800 of 10419 papers

TitleStatusHype
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed DatasetsCode1
Layer-adaptive sparsity for the Magnitude-based PruningCode1
An Enhanced Scheme for Reducing the Complexity of Pointwise Convolutions in CNNs for Image Classification Based on Interleaved Grouped Filters without Divisibility ConstraintsCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
Breast Cancer Histopathology Image Classification and Localization using Multiple Instance LearningCode1
An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit RecognitionCode1
Leveraging Vision-Language Models for Improving Domain Generalization in Image ClassificationCode1
Efficient Classification of Very Large Images with Tiny ObjectsCode1
A Unified Algebraic Perspective on Lipschitz Neural NetworksCode1
A Neural Dirichlet Process Mixture Model for Task-Free Continual LearningCode1
Bridging the Gap: Multi-Level Cross-Modality Joint Alignment for Visible-Infrared Person Re-IdentificationCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
Deep Network Ensemble Learning applied to Image Classification using CNN TreesCode1
BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold NetworksCode1
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image ClassificationCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
ELSA: Enhanced Local Self-Attention for Vision TransformerCode1
A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain AdaptationCode1
Emerging Properties in Self-Supervised Vision TransformersCode1
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz NetworksCode1
Cached Transformers: Improving Transformers with Differentiable Memory CacheCode1
Fcaformer: Forward Cross Attention in Hybrid Vision TransformerCode1
Encoder-Decoder with Atrous Separable Convolution for Semantic Image SegmentationCode1
AugMix: A Simple Data Processing Method to Improve Robustness and UncertaintyCode1
Deep Networks with Stochastic DepthCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified