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 19511975 of 10420 papers

TitleStatusHype
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image ClassificationCode1
Are Natural Domain Foundation Models Useful for Medical Image Classification?Code1
Gradient-based Parameter Selection for Efficient Fine-TuningCode1
GenFormer -- Generated Images are All You Need to Improve Robustness of Transformers on Small DatasetsCode1
Multi-Scale High-Resolution Vision Transformer for Semantic SegmentationCode1
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
Swin Transformer V2: Scaling Up Capacity and ResolutionCode1
Switch EMA: A Free Lunch for Better Flatness and SharpnessCode1
GLiT: Neural Architecture Search for Global and Local Image TransformerCode1
GEVO: GPU Code Optimization using Evolutionary ComputationCode1
TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video SurveillanceCode1
GhostNet: More Features from Cheap OperationsCode1
HS-ResNet: Hierarchical-Split Block on Convolutional Neural NetworkCode1
Fully Hyperbolic Convolutional Neural Networks for Computer VisionCode1
Are These Birds Similar: Learning Branched Networks for Fine-grained RepresentationsCode1
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image ClassificationCode1
HRFormer: High-Resolution Transformer for Dense PredictionCode1
Learning Hierarchical Image Segmentation For Recognition and By RecognitionCode1
Global Filter Networks for Image ClassificationCode1
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image ClassificationCode1
Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance?Code1
Going Deeper with ConvolutionsCode1
HRN: A Holistic Approach to One Class LearningCode1
Task-Oriented Feature DistillationCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
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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