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 201225 of 10419 papers

TitleStatusHype
MogaNet: Multi-order Gated Aggregation NetworkCode2
WITT: A Wireless Image Transmission Transformer for Semantic CommunicationsCode2
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View CompletionCode2
The Equalization Losses: Gradient-Driven Training for Long-tailed Object RecognitionCode2
MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input FeaturesCode2
Spikformer: When Spiking Neural Network Meets TransformerCode2
Dilated Neighborhood Attention TransformerCode2
Generalized Parametric Contrastive LearningCode2
Mega: Moving Average Equipped Gated AttentionCode2
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image ClassificationCode2
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language ModelsCode2
What does a platypus look like? Generating customized prompts for zero-shot image classificationCode2
No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small ObjectsCode2
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated ConvolutionsCode2
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image ClassificationCode2
Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial ScenariosCode2
Wave-ViT: Unifying Wavelet and Transformers for Visual Representation LearningCode2
Shifts 2.0: Extending The Dataset of Real Distributional ShiftsCode2
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision ApplicationsCode2
Global Context Vision TransformersCode2
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEsCode2
Neural Prompt SearchCode2
MobileOne: An Improved One millisecond Mobile BackboneCode2
Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature DistillationCode2
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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