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 83518375 of 10420 papers

TitleStatusHype
MACE: Model Agnostic Concept Extractor for Explaining Image Classification NetworksCode0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
Automatic Open-World Reliability AssessmentCode0
Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label SmoothingCode0
Gall Bladder Cancer Detection from US Images with Only Image Level LabelsCode0
Evolving Deep Convolutional Neural Networks for Image ClassificationCode0
Game of Gradients: Mitigating Irrelevant Clients in Federated LearningCode0
Evolutionary NAS with Gene Expression Programming of Cellular EncodingCode0
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGradCode0
Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning RatesCode0
On the Improvement of Generalization and Stability of Forward-Only Learning via Neural PolarizationCode0
Removing the Feature Correlation Effect of Multiplicative NoiseCode0
Evaluation of Output Embeddings for Fine-Grained Image ClassificationCode0
Gated Channel Transformation for Visual RecognitionCode0
Gated Convolutional Networks with Hybrid Connectivity for Image ClassificationCode0
Gated Linear NetworksCode0
Gated Recurrent Convolution Neural Network for OCRCode0
ReNet: A Recurrent Neural Network Based Alternative to Convolutional NetworksCode0
Evaluation of Explanation Methods of AI -- CNNs in Image Classification Tasks with Reference-based and No-reference MetricsCode0
Gaussian-Based Pooling for Convolutional Neural NetworksCode0
Machine Learning Models that Remember Too MuchCode0
Machine Learning State-of-the-Art with UncertaintiesCode0
Evaluation and Comparison of Visual Language Models for Transportation Engineering ProblemsCode0
Anchor Loss: Modulating Loss Scale based on Prediction DifficultyCode0
Evaluating the Performance of TAAF for image classification modelsCode0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified