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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4676–4700 of 10420 papers

TitleStatusHype
CNN depth analysis with different channel inputs for Acoustic Scene Classification—0
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet—0
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths—0
Accelerator-aware Neural Network Design using AutoML—0
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments—0
DaTscan SPECT Image Classification for Parkinson's Disease—0
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations—0
Data-Side Efficiencies for Lightweight Convolutional Neural Networks—0
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks—0
A Linear Approximation to the chi^2 Kernel with Geometric Convergence—0
Improved Techniques for Adversarial Discriminative Domain Adaptation—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases—0
Dataset Distillation for Histopathology Image Classification—0
Exploring Localization for Self-supervised Fine-grained Contrastive Learning—0
Adapting to Label Shift with Bias-Corrected Calibration—0
Improve Unsupervised Domain Adaptation with Mixup Training—0
Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification—0
Dataset Bias Prediction for Few-Shot Image Classification—0
Data Representation using the Weyl Transform—0
Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks—0
Improvement of image classification by multiple optical scattering—0
AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified