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 87268750 of 10420 papers

TitleStatusHype
Optimizing Neural Networks with Gradient Lexicase SelectionCode0
Dynamic Mobile-Former: Strengthening Dynamic Convolution with Attention and Residual Connection in Kernel SpaceCode0
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networksCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Histogram Layers for Neural Engineered FeaturesCode0
AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain ShiftCode0
Concept-based explainability for an EEG transformer modelCode0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Histopathological Image Classification using Discriminative Feature-oriented Dictionary LearningCode0
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of ResourcesCode0
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual RecognitionCode0
Computer-aided Interpretable Features for Leaf Image ClassificationCode0
Dynamic Loss For Robust LearningCode0
Quality-Agnostic Image Recognition via Invertible DecoderCode0
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster InferenceCode0
MetH: A family of high-resolution and variable-shape image challengesCode0
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image ClassifiersCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium OrchidsCode0
Dynamic Convolution: Attention over Convolution KernelsCode0
Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labelsCode0
Quality Assessment of In-the-Wild VideosCode0
Deep and interpretable regression models for ordinal outcomesCode0
Metrics and methods for robustness evaluation of neural networks with generative modelsCode0
Compressing Vision Transformers for Low-Resource Visual LearningCode0
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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
10RevCol-HTop 1 Accuracy90Unverified