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

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Showing 10401–10420 of 10420 papers

TitleStatusHype
Maximal function pooling with applications—0
Maximal Independent Sets for Pooling in Graph Neural Networks—0
Maximal Independent Vertex Set applied to Graph Pooling—0
Maximal Jacobian-based Saliency Map Attack—0
Maximum Categorical Cross Entropy (MCCE): A noise-robust alternative loss function to mitigate racial bias in Convolutional Neural Networks (CNNs) by reducing overfitting—0
Maximum Entropy Regularization and Chinese Text Recognition—0
Maxmin convolutional neural networks for image classification—0
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training—0
Mayfly optimization with deep learning enabled retinal fundus image classification model—0
MBInception: A new Multi-Block Inception Model for Enhancing Image Processing Efficiency—0
MCFNet: A Multimodal Collaborative Fusion Network for Fine-Grained Semantic Classification—0
MCNet: A crowd denstity estimation network based on integrating multiscale attention module—0
MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning—0
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets—0
TorchQL: A Programming Framework for Integrity Constraints in Machine Learning—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
MDL-NAS: A Joint Multi-Domain Learning Framework for Vision Transformer—0
Measuring directional bias amplification in image captions using predictability—0
Measuring Model Biases in the Absence of Ground Truth—0
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves—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