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 150 of 10419 papers

TitleStatusHype
MambaVision: A Hybrid Mamba-Transformer Vision BackboneCode7
MambaOut: Do We Really Need Mamba for Vision?Code7
Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image AnalysisCode7
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learningCode7
Mini-Gemini: Mining the Potential of Multi-modality Vision Language ModelsCode7
AutoTrain: No-code training for state-of-the-art modelsCode7
Visual-RFT: Visual Reinforcement Fine-TuningCode7
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and ResolutionCode6
DINOv2: Learning Robust Visual Features without SupervisionCode6
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-AwarenessCode6
Visual Instruction TuningCode6
Improved Baselines with Visual Instruction TuningCode6
A ConvNet for the 2020sCode5
Efficient Multimodal Learning from Data-centric PerspectiveCode5
Multimodal Autoregressive Pre-training of Large Vision EncodersCode5
Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes InteractivelyCode5
Scalable Pre-training of Large Autoregressive Image ModelsCode5
Sequencer: Deep LSTM for Image ClassificationCode5
Chinese CLIP: Contrastive Vision-Language Pretraining in ChineseCode5
MedMamba: Vision Mamba for Medical Image ClassificationCode4
Catastrophic Forgetting in Deep Learning: A Comprehensive TaxonomyCode4
Wavelet Convolutions for Large Receptive FieldsCode4
Kolmogorov-Arnold Convolutions: Design Principles and Empirical StudiesCode4
InceptionNeXt: When Inception Meets ConvNeXtCode4
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable ConvolutionsCode4
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One DayCode4
Kolmogorov-Arnold TransformerCode4
Vision GNN: An Image is Worth Graph of NodesCode4
Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like ArchitecturesCode4
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual ModelsCode4
Visual Attention NetworkCode4
Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal LearningCode4
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision ApplicationsCode4
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment AnythingCode4
AltCLIP: Altering the Language Encoder in CLIP for Extended Language CapabilitiesCode4
Detectron2 Object Detection & Manipulating Images using CartoonizationCode4
OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic KernelsCode4
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and VideoCode4
A Framework For Contrastive Self-Supervised Learning And Designing A New ApproachCode4
Architecture-Agnostic Masked Image Modeling -- From ViT back to CNNCode4
Efficient Post-training Quantization with FP8 FormatsCode4
Benchopt: Reproducible, efficient and collaborative optimization benchmarksCode4
Deep Residual Learning for Image RecognitionCode4
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense PredictionCode4
RegNet: Self-Regulated Network for Image ClassificationCode4
MaxViT: Multi-Axis Vision TransformerCode3
MetaFormer Baselines for VisionCode3
Ludwig: a type-based declarative deep learning toolboxCode3
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive SurveyCode3
Cascade Prompt Learning for Vision-Language Model AdaptationCode3
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified