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 94019425 of 10420 papers

TitleStatusHype
Generative Data Mining with Longtail-Guided Diffusion0
Generatively Augmented Neural Network Watchdog for Image Classification Networks0
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints0
Generative Negative Text Replay for Continual Vision-Language Pretraining0
Generative NeuroEvolution for Deep Learning0
Generic Image Classification Approaches Excel on Face Recognition0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition0
GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware0
Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification0
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing0
Geodesics of learned representations0
Geometric Mean Improves Loss For Few-Shot Learning0
Geometric Median Matching for Robust k-Subset Selection from Noisy Data0
Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons0
Geometric Scattering for Graph Data Analysis0
Geometry aware convolutional filters for omnidirectional images representation0
GeoNet: Benchmarking Unsupervised Adaptation across Geographies0
GeoTop: Advancing Image Classification with Geometric-Topological Analysis0
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images0
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning0
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design0
G-EvoNAS: Evolutionary Neural Architecture Search Based on Network Growth0
Ghost Loss to Question the Reliability of Training Data0
GhostNetV3: Exploring the Training Strategies for Compact Models0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified