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 43264350 of 10420 papers

TitleStatusHype
Fully trainable Gaussian derivative convolutional layerCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Fractional Max-PoolingCode0
FractalNet: Ultra-Deep Neural Networks without ResidualsCode0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
Assisted Perception: Optimizing Observations to Communicate StateCode0
Context-Aware Compilation of DNN Training Pipelines across Edge and CloudCode0
Continual Learning with Deep Streaming Regularized Discriminant AnalysisCode0
Fixed-Point Convolutional Neural Network for Real-Time Video Processing in FPGACode0
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networksCode0
High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature MapsCode0
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical ImagingCode0
Histogram Layers for Neural Engineered FeaturesCode0
High Definition image classification in Geoscience using Machine LearningCode0
Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action RecognitionCode0
High-fidelity Pseudo-labels for Boosting Weakly-Supervised SegmentationCode0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active LearningCode0
Fossil Image Identification using Deep Learning Ensembles of Data Augmented MultiviewsCode0
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion ReductionCode0
ImageNot: A contrast with ImageNet preserves model rankingsCode0
Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image ClassificationCode0
Continuous Meta-Learning without TasksCode0
Hiera: A Hierarchical Vision Transformer without the Bells-and-WhistlesCode0
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and BeyondCode0
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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