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 45014525 of 10420 papers

TitleStatusHype
CoNe: Contrast Your Neighbours for Supervised Image ClassificationCode0
Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail RecognitionCode0
A Simple Single-Scale Vision Transformer for Object Localization and Instance SegmentationCode0
Harnessing Adversarial Distances to Discover High-Confidence ErrorsCode0
Harnessing the Power of Infinitely Wide Deep Nets on Small-data TasksCode0
HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image ClassificationCode0
Conditional Variance Penalties and Domain Shift RobustnessCode0
Fine-Grained Scene Image Classification with Modality-Agnostic AdapterCode0
Cooperative Meta-Learning with Gradient AugmentationCode0
Learnable Adaptive Cosine Estimator (LACE) for Image ClassificationCode0
Activation Function Optimization Scheme for Image ClassificationCode0
Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone NetworksCode0
Fine-grained Optimization of Deep Neural NetworksCode0
Glyce: Glyph-vectors for Chinese Character RepresentationsCode0
Hardware Resilience Properties of Text-Guided Image ClassifiersCode0
Fine-Grained ImageNet Classification in the WildCode0
Learning Accurate Performance Predictors for Ultrafast Automated Model CompressionCode0
Learning Activation Functions to Improve Deep Neural NetworksCode0
Learning by Self-ExplainingCode0
H²O: Heatmap by Hierarchical OcclusionCode0
Hard Negative Sample Mining for Whole Slide Image ClassificationCode0
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial AttacksCode0
Guarantees of confidentiality via Hammersley-Chapman-Robbins boundsCode0
Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time AppsCode0
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain GeneralizationCode0
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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