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 8651–8675 of 10420 papers

TitleStatusHype
Distribution-Aware Adaptive Multi-Bit Quantization—0
Distribution Learning Based on Evolutionary Algorithm Assisted Deep Neural Networks for Imbalanced Image Classification—0
Distribution-sensitive Information Retention for Accurate Binary Neural Network—0
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training—0
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions—0
Divergent Search for Few-Shot Image Classification—0
Diverse Feature Learning by Self-distillation and Reset—0
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning—0
Diversifying Sample Generation for Accurate Data-Free Quantization—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
Diversity Matters When Learning From Ensembles—0
Diving into Optimization of Topology in Neural Networks—0
DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)—0
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning—0
DM-CT: Consistency Training with Data and Model Perturbation—0
DMSANet: Dual Multi Scale Attention Network—0
Do Better ImageNet Models Transfer Better?—0
Do Convnets Learn Correspondence?—0
Do Convolutional Neural Networks Learn Class Hierarchy?—0
Document AI: Benchmarks, Models and Applications—0
Document image classification, with a specific view on applications of patent images—0
DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification—0
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks—0
Does Data Augmentation Benefit from Split BatchNorms—0
Does deep learning model calibration improve performance in class-imbalanced medical 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
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