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 37763800 of 10420 papers

TitleStatusHype
AutoFCL: Automatically Tuning Fully Connected Layers for Handling Small DatasetCode0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network EnsembleCode0
Adaptive Activation Functions for Predictive Modeling with Sparse Experimental DataCode0
Image classification in frequency domain with 2SReLU: a second harmonics superposition activation functionCode0
Image Classification Using Singular Value Decomposition and OptimizationCode0
ComFe: Interpretable Image Classifiers With Foundation Models, Transformers and Component FeaturesCode0
Dataset Distillation with Infinitely Wide Convolutional NetworksCode0
CNN Features off-the-shelf: an Astounding Baseline for RecognitionCode0
Dataset Distillation using Neural Feature RegressionCode0
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationCode0
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image SegmentationCode0
Explaining Domain Shifts in Language: Concept erasing for Interpretable Image ClassificationCode0
Dataset Condensation with Differentiable Siamese AugmentationCode0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
Image-Caption Encoding for Improving Zero-Shot GeneralizationCode0
Dataset Condensation Driven Machine UnlearningCode0
Explaining NonLinear Classification Decisions with Deep Taylor DecompositionCode0
ScaleNet: Scale Invariance Learning in Directed GraphsCode0
IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural NetworksCode0
Distilling Effective Supervision from Severe Label NoiseCode0
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain AdaptationCode0
Image classification and retrieval with random depthwise signed convolutional neural networksCode0
Data Representations' Study of Latent Image ManifoldsCode0
AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language ModelsCode0
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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