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 3151–3175 of 10420 papers

TitleStatusHype
Diving into Optimization of Topology in Neural Networks—0
Bombus Species Image Classification—0
DISCO: Distributed Inference with Sparse Communications—0
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning—0
Block-wise Scrambled Image Recognition Using Adaptation Network—0
Disaggregated Deep Learning via In-Physics Computing at Radio Frequency—0
DM-CT: Consistency Training with Data and Model Perturbation—0
DMSANet: Dual Multi Scale Attention Network—0
Block-term Tensor Neural Networks—0
Do Better ImageNet Models Transfer Better?—0
An Efficient Technique for Image Captioning using Deep Neural Network—0
Do Convnets Learn Correspondence?—0
Explanation of Unintended Radiated Emission Classification via LIME—0
Document AI: Benchmarks, Models and Applications—0
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models—0
Document image classification, with a specific view on applications of patent images—0
DiSa: Directional Saliency-Aware Prompt Learning for Generalizable Vision-Language Models—0
Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures—0
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks—0
Blockout: Dynamic Model Selection for Hierarchical Deep Networks—0
Explaining Representation by Mutual Information—0
Does Data Augmentation Benefit from Split BatchNorms—0
An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification—0
Does deep learning model calibration improve performance in class-imbalanced medical image classification?—0
Dirichlet-based Histogram Feature Transform for Image Classification—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
10RevCol-HTop 1 Accuracy90—Unverified