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 1851–1875 of 10420 papers

TitleStatusHype
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods—0
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning—0
Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image ClassificationCode0
Distilled Gradual Pruning with Pruned Fine-tuningCode0
What to Do When Your Discrete Optimization Is the Size of a Neural Network?Code0
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical HeterogeneityCode0
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment—0
How Flawed Is ECE? An Analysis via Logit SmoothingCode0
Balancing the Causal Effects in Class-Incremental Learning—0
Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification—0
ViGEO: an Assessment of Vision GNNs in Earth ObservationCode0
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing DiffusionCode0
I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption—0
Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lagCode0
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models—0
Switch EMA: A Free Lunch for Better Flatness and SharpnessCode1
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs—0
APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks—0
Contrastive Learning for Regression on Hyperspectral Data—0
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging ClassificationCode0
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensembleCode0
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense—0
A novel spatial-frequency domain network for zero-shot incremental learning—0
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation—0
Latent Enhancing AutoEncoder for Occluded Image Classification—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