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 12761300 of 10419 papers

TitleStatusHype
Gradient-based Class Weighting for Unsupervised Domain Adaptation in Dense Prediction Visual Tasks0
Semantic Compositions Enhance Vision-Language Contrastive Learning0
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuningCode1
Optimized Learning for X-Ray Image Classification for Multi-Class Disease Diagnoses with Accelerated Computing Strategies0
Scarecrow monitoring system:employing mobilenet ssd for enhanced animal supervision0
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
Formal Verification of Deep Neural Networks for Object Detection0
xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba CounterpartCode3
Rescaling Large Datasets Based on Validation Outcomes of a Pre-trained NetworkCode0
Kolmogorov-Arnold Convolutions: Design Principles and Empirical StudiesCode4
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical ImagesCode1
ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks0
Image Classification for Snow Detection to Improve Pedestrian Safety0
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent CollaborationCode2
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review0
Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data RegimesCode0
RepAct: The Re-parameterizable Adaptive Activation FunctionCode0
Instance Temperature Knowledge DistillationCode0
FedMLP: Federated Multi-Label Medical Image Classification under Task HeterogeneityCode1
Learning Visual Conditioning Tokens to Correct Domain Shift for Fully Test-time Adaptation0
Adaptive Stochastic Weight AveragingCode0
Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across HeadsCode1
ViT-1.58b: Mobile Vision Transformers in the 1-bit EraCode1
Optimization of Autonomous Driving Image Detection Based on RFAConv and Triplet Attention0
TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification0
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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