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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5251–5275 of 10420 papers

TitleStatusHype
Enhanced Gradient for Differentiable Architecture Search—0
Enhanced Attacks on Defensively Distilled Deep Neural Networks—0
Classification and Retrieval of Digital Pathology Scans: A New Dataset—0
An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning—0
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning—0
Classification and reconstruction of spatially overlapping phase images using diffractive optical networks—0
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels—0
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning—0
Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses—0
Adversarial attacks to image classification systems using evolutionary algorithms—0
Energy Efficient Hadamard Neural Networks—0
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees—0
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent—0
Class Distance Weighted Cross Entropy Loss for Classification of Disease Severity—0
Energy-efficient Amortized Inference with Cascaded Deep Classifiers—0
Energy-constrained Self-training for Unsupervised Domain Adaptation—0
Energy-Based Spherical Sparse Coding—0
End-to-end training of deep kernel map networks for image classification—0
A Novel Weight-Shared Multi-Stage CNN for Scale Robustness—0
Adversarial Attacks to Direct Data-driven Control for Destabilization—0
A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches—0
A 1Mb mixed-precision quantized encoder for image classification and patch-based compression—0
End-to-end optimized image compression for multiple machine tasks—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified