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

Showing 101125 of 10419 papers

TitleStatusHype
FixMatch: Simplifying Semi-Supervised Learning with Consistency and ConfidenceCode2
Fixing the train-test resolution discrepancyCode2
FasterViT: Fast Vision Transformers with Hierarchical AttentionCode2
Fixing the train-test resolution discrepancy: FixEfficientNetCode2
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
HAIR: Hypernetworks-based All-in-One Image RestorationCode2
EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space DualityCode2
Efficient Multi-Scale Attention Module with Cross-Spatial LearningCode2
MogaNet: Multi-order Gated Aggregation NetworkCode2
UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene ImageryCode2
ERS: a novel comprehensive endoscopy image dataset for machine learning, compliant with the MST 3.0 specificationCode2
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural NetworksCode2
Fast Vision Transformers with HiLo AttentionCode2
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and TransformerCode2
Dilated Neighborhood Attention TransformerCode2
Focal Modulation NetworksCode2
Frontiers in Intelligent ColonoscopyCode2
AdaFisher: Adaptive Second Order Optimization via Fisher InformationCode2
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed GradientsCode2
Generalized Parametric Contrastive LearningCode2
Generative Pretraining from PixelsCode2
Global Context Vision TransformersCode2
Adapter is All You Need for Tuning Visual TasksCode2
3D-RCNet: Learning from Transformer to Build a 3D Relational ConvNet for Hyperspectral Image ClassificationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified