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 7201–7225 of 10420 papers

TitleStatusHype
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation—0
Model-Agnostic Learning to Meta-Learn—0
Matching Distributions via Optimal Transport for Semi-Supervised Learning—0
Batch Group Normalization—0
Evolving Character-Level DenseNet Architectures using Genetic Programming—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclusteringCode0
Artist, Style And Year Classification Using Face Recognition And Clustering With Convolutional Neural Networks—0
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?—0
Chair Segments: A Compact Benchmark for the Study of Object SegmentationCode0
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution—0
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs—0
Communication-Efficient Federated Distillation—0
Adversarial Robustness Across Representation Spaces—0
One-sample Guided Object Representation Disassembling—0
Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNetsCode0
Self-Supervised Generative Adversarial Compression—0
Learning Invariances in Neural Networks from Training Data—0
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point—0
A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data—0
Rethinking Skip Connection with Layer Normalization—0
Generalized Boosting—0
Scale-covariant and scale-invariant Gaussian derivative networks—0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
Reducing Textural Bias Improves Robustness of Deep Segmentation Models—0
Show:102550
← PrevPage 289 of 417Next →
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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified