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 76100 of 10419 papers

TitleStatusHype
Visual Prompt TuningCode3
QOC: Quantum On-Chip Training with Parameter Shift and Gradient PruningCode3
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation ModelsCode3
Patches Are All You Need?Code3
Transformers in Medical Imaging: A SurveyCode3
Detecting Twenty-thousand Classes using Image-level SupervisionCode3
Datasets: A Community Library for Natural Language ProcessingCode3
XCiT: Cross-Covariance Image TransformersCode3
EfficientNetV2: Smaller Models and Faster TrainingCode3
U^2-Net: Going Deeper with Nested U-Structure for Salient Object DetectionCode3
ResNeSt: Split-Attention NetworksCode3
Momentum Contrast for Unsupervised Visual Representation LearningCode3
Ludwig: a type-based declarative deep learning toolboxCode3
EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksCode3
Bag of Freebies for Training Object Detection Neural NetworksCode3
AutoAugment: Learning Augmentation Policies from DataCode3
GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language ModelsCode2
Optimal Weighted Convolution for Classification and DenosingCode2
Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information AnalysisCode2
Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-TuningCode2
UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly DetectionCode2
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization AlignmentCode2
Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood AttentionCode2
DAMamba: Vision State Space Model with Dynamic Adaptive ScanCode2
ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image ClassificationCode2
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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