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 301325 of 10419 papers

TitleStatusHype
GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNsCode2
Adapter is All You Need for Tuning Visual TasksCode2
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense PredictionCode2
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text LabelsCode2
Transferability of Adversarial Examples to Attack Cloud-based Image Classifier ServiceCode2
CLIP-Art: Contrastive Pre-training for Fine-Grained Art ClassificationCode2
CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet UpcyclingCode2
Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-TuningCode2
K-LITE: Learning Transferable Visual Models with External KnowledgeCode2
CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale AttentionCode2
Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image ClassificationCode2
ktrain: A Low-Code Library for Augmented Machine LearningCode2
Big Transfer (BiT): General Visual Representation LearningCode2
LayoutLM: Pre-training of Text and Layout for Document Image UnderstandingCode2
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent CollaborationCode2
Learning Transferable Visual Models From Natural Language SupervisionCode2
Class-Aware Contrastive Semi-Supervised LearningCode1
Class Adaptive Network CalibrationCode1
Class-Aware Patch Embedding Adaptation for Few-Shot Image ClassificationCode1
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image ClassificationCode1
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion AutoencoderCode1
Circumventing Outliers of AutoAugment with Knowledge DistillationCode1
Class-Balanced Active Learning for Image ClassificationCode1
An In-depth Study of Stochastic BackpropagationCode1
CheXWorld: Exploring Image World Modeling for Radiograph Representation LearningCode1
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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