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 22762300 of 10420 papers

TitleStatusHype
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset0
Semantic Generative Augmentations for Few-Shot CountingCode1
Sliceformer: Make Multi-head Attention as Simple as Sorting in Discriminative TasksCode0
torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
A Survey on Transferability of Adversarial Examples across Deep Neural NetworksCode1
MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited MemoryCode1
Sanity checks for patch visualisation in prototype-based image classification0
A Multi-Modal Multilingual Benchmark for Document Image Classification0
Stochastic Gradient Sampling for Enhancing Neural Networks Training0
Interferometric Neural NetworksCode0
Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models0
Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs0
Vision-Language Pseudo-Labels for Single-Positive Multi-Label LearningCode1
Interpretable Medical Image Classification using Prototype Learning and Privileged InformationCode1
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge0
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles0
Federated learning compression designed for lightweight communicationsCode0
Delayed Memory Unit: Modelling Temporal Dependency Through Delay GateCode0
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding0
SAMCLR: Contrastive pre-training on complex scenes using SAM for view sampling0
Are LSTMs Good Few-Shot Learners?Code0
Conditional Consistency Regularization for Semi-Supervised Multi-label Image Classification0
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement0
Boosting for Bounding the Worst-class Error0
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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