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 1010110125 of 10420 papers

TitleStatusHype
Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting0
Embedding Label Structures for Fine-Grained Feature Representation0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels0
Explaining NonLinear Classification Decisions with Deep Taylor DecompositionCode0
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition0
Learning The Structure of Deep Convolutional Networks0
Aggregating Local Deep Features for Image Retrieval0
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks0
MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking0
Task-Driven Feature Pooling for Image Classification0
Structured Feature Selection0
Design of Kernels in Convolutional Neural Networks for Image ClassificationCode0
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks0
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification0
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)Code0
Recombinator Networks: Learning Coarse-to-Fine Feature AggregationCode0
Auxiliary Image Regularization for Deep CNNs with Noisy Labels0
Data-dependent Initializations of Convolutional Neural NetworksCode0
Top-k Multiclass SVMCode0
Training CNNs with Low-Rank Filters for Efficient Image Classification0
Semantic Diversity versus Visual Diversity in Visual Dictionaries0
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial NetworksCode0
Geodesics of learned representations0
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?0
Compact Bilinear PoolingCode0
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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