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 5901–5925 of 10420 papers

TitleStatusHype
Self-Training: A Survey—0
Mixed-Block Neural Architecture Search for Medical Image Segmentation—0
Deep Learning Reproducibility and Explainable AI (XAI)—0
Efficient and Differentiable Conformal Prediction with General Function ClassesCode0
Cut and Continuous Paste towards Real-time Deep Fall Detection—0
Retrieval Augmented Classification for Long-Tail Visual Recognition—0
Rethinking the Zigzag Flattening for Image Reading—0
On the Implicit Bias Towards Minimal Depth of Deep Neural Networks—0
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates—0
Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks—0
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks—0
MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition—0
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation—0
Dynamic Object Comprehension: A Framework For Evaluating Artificial Visual Perception—0
Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification—0
Generalizable Information Theoretic Causal Representation—0
Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars—0
Unified smoke and fire detection in an evolutionary framework with self-supervised progressive data augment—0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision—0
Meta Knowledge Distillation—0
Measuring Unintended Memorisation of Unique Private Features in Neural Networks—0
Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition—0
A precortical module for robust CNNs to light variations—0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
A Theory of PAC Learnability under Transformation Invariances—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
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