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 41764200 of 10420 papers

TitleStatusHype
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNNCode0
Active Learning via Classifier Impact and Greedy Selection for Interactive Image RetrievalCode0
Hyperspectral Image Classification in the Presence of Noisy LabelsCode0
Hyperspectral image classification via a random patches networkCode0
Hyperparameter Ensembles for Robustness and Uncertainty QuantificationCode0
A Synaptic Neural Network and Synapse LearningCode0
Convolutional Oriented Boundaries: From Image Segmentation to High-Level TasksCode0
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature EmbeddingCode0
Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape AnalysisCode0
Convolutional Oriented BoundariesCode0
Convolutional Neural Networks with Layer ReuseCode0
Automated Classification of Histopathology Images Using Transfer LearningCode0
Hyperspectral Image Classification via Sparse Representation With Incremental DictionariesCode0
Identifying Adversarially Attackable and Robust SamplesCode0
Immiscible Color Flows in Optimal Transport Networks for Image ClassificationCode0
Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite ImageryCode0
Asymmetric Masked Distillation for Pre-Training Small Foundation ModelsCode0
HyenaPixel: Global Image Context with ConvolutionsCode0
Convolutional Neural Networks combined with Runge-Kutta MethodsCode0
Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural NetworksCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Human-imperceptible, Machine-recognizable ImagesCode0
Human-in-the-Loop Visual Re-ID for Population Size EstimationCode0
HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image ClassificationCode0
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral ImageCode0
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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