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 26762700 of 10420 papers

TitleStatusHype
Improving the Gating Mechanism of Recurrent Neural NetworksCode0
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasetsCode0
An In-Depth Analysis of Adversarial Discriminative Domain Adaptation for Digit ClassificationCode0
Can a Confident Prior Replace a Cold Posterior?Code0
3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image ClassificationCode0
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human ExpertsCode0
Improving the trustworthiness of image classification models by utilizing bounding-box annotationsCode0
CAMP: Continuous and Adaptive Learning Model in PathologyCode0
An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)Code0
Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase SamplingCode0
CAM-Based Methods Can See through WallsCode0
Improving robustness to corruptions with multiplicative weight perturbationsCode0
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural NetworksCode0
An Image Patch is a Wave: Phase-Aware Vision MLPCode0
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural NetworksCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
Calibrating Deep Convolutional Gaussian ProcessesCode0
Calibrate to InterpretCode0
Angle based dynamic learning rate for gradient descentCode0
Calibrated Selective ClassificationCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
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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