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 27012725 of 10420 papers

TitleStatusHype
Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase SamplingCode0
A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation ProcessCode0
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball ComputingCode0
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Byzantine-Robust Aggregation for Securing Decentralized Federated LearningCode0
A New Loss Function for CNN Classifier Based on Pre-defined Evenly-Distributed Class CentroidsCode0
BViT: Broad Attention based Vision TransformerCode0
GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric CancerCode0
A Direct Approach to Robust Deep Learning Using Adversarial NetworksCode0
Improving robustness to corruptions with multiplicative weight perturbationsCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Efficient Ladder-style DenseNets for Semantic Segmentation of Large ImagesCode0
Building Optimal Neural Architectures using Interpretable KnowledgeCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and RetrainingCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human ExpertsCode0
Understanding Intrinsic Robustness Using Label UncertaintyCode0
Intelligent Multi-View Test Time AugmentationCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet CategoriesCode0
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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