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 426450 of 10419 papers

TitleStatusHype
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion AutoencoderCode1
Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image ClassificationCode1
LADDER: Language Driven Slice Discovery and Error RectificationCode1
Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small DatasetsCode1
SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image ClassificationCode1
Mew: Multiplexed Immunofluorescence Image Analysis through an Efficient Multiplex NetworkCode1
LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification TaskCode1
Augmented Neural Fine-Tuning for Efficient Backdoor PurificationCode1
On Exact Bit-level Reversible Transformers Without Changing ArchitecturesCode1
BiasPruner: Debiased Continual Learning for Medical Image ClassificationCode1
Histopathological Image Classification with Cell Morphology Aware Deep Neural NetworksCode1
GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image ClassificationCode1
Trainable Highly-expressive Activation FunctionsCode1
Dual-stage Hyperspectral Image Classification Model with Spectral SupertokenCode1
Multi-Label Plant Species Classification with Self-Supervised Vision TransformersCode1
Momentum Auxiliary Network for Supervised Local LearningCode1
HyperKAN: Kolmogorov-Arnold Networks make Hyperspectral Image Classificators SmarterCode1
PDiscoFormer: Relaxing Part Discovery Constraints with Vision TransformersCode1
reBEN: Refined BigEarthNet Dataset for Remote Sensing Image AnalysisCode1
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical ImagesCode1
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuningCode1
Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across HeadsCode1
FedMLP: Federated Multi-Label Medical Image Classification under Task HeterogeneityCode1
ViT-1.58b: Mobile Vision Transformers in the 1-bit EraCode1
Implicit-Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D Images and 3D ScenesCode1
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified