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 15011525 of 10419 papers

TitleStatusHype
BitQ: Tailoring Block Floating Point Precision for Improved DNN Efficiency on Resource-Constrained DevicesCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
DAM: Dynamic Adapter Merging for Continual Video QA LearningCode1
A General Regret Bound of Preconditioned Gradient Method for DNN TrainingCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
Knowledge Diffusion for DistillationCode1
Data Augmentation with norm-VAE for Unsupervised Domain AdaptationCode1
Denoised Smoothing: A Provable Defense for Pretrained ClassifiersCode1
Knowledge Distillation Using Hierarchical Self-Supervision Augmented DistributionCode1
DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character RecognitionCode1
CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution LayersCode1
CvT: Introducing Convolutions to Vision TransformersCode1
Benchmarking Knowledge-driven Zero-shot LearningCode1
Label Propagation for Zero-shot Classification with Vision-Language ModelsCode1
Understanding the Role of the Projector in Knowledge DistillationCode1
LADDER: Language Driven Slice Discovery and Error RectificationCode1
Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image ClassificationCode1
Language Models as Black-Box Optimizers for Vision-Language ModelsCode1
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesCode1
Large-scale Dataset Pruning with Dynamic UncertaintyCode1
"BNN - BN = ?": Training Binary Neural Networks without Batch NormalizationCode1
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image ClassificationCode1
Boosting Active Learning via Improving Test PerformanceCode1
Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window AttentionCode1
CycleMLP: A MLP-like Architecture for Dense PredictionCode1
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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