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 99519975 of 10420 papers

TitleStatusHype
TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin MachinesCode0
TLAC: Two-stage LMM Augmented CLIP for Zero-Shot ClassificationCode0
Tiny Updater: Towards Efficient Neural Network-Driven Software UpdatingCode0
Tiny models from tiny data: Textual and null-text inversion for few-shot distillationCode0
Selective Replay Enhances Learning in Online Continual Analogical ReasoningCode0
Token Turing Machines are Efficient Vision ModelsCode0
Tom: Leveraging trend of the observed gradients for faster convergenceCode0
What Do You See? Enhancing Zero-Shot Image Classification with Multimodal Large Language ModelsCode0
Integration of Leaky-Integrate-and-Fire-Neurons in Deep Learning ArchitecturesCode0
Debiased Noise Editing on Foundation Models for Fair Medical Image ClassificationCode0
Top-k Multiclass SVMCode0
TopoAct: Visually Exploring the Shape of Activations in Deep LearningCode0
Spiking Deep Networks with LIF NeuronsCode0
Tiny Models are the Computational Saver for Large ModelsCode0
Timage -- A Robust Time Series Classification PipelineCode0
Topology Optimization of Random Memristors for Input-Aware Dynamic SNNCode0
SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural NetworkCode0
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised LearningCode0
Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization LayersCode0
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural NetworkCode0
Visualizing and Understanding Convolutional NetworksCode0
SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural NetworksCode0
Wide-Slice Residual Networks for Food RecognitionCode0
Threshold Modulation for Online Test-Time Adaptation of Spiking Neural NetworksCode0
Adaptive occlusion sensitivity analysis for visually explaining video recognition networksCode0
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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
10RevCol-HTop 1 Accuracy90Unverified