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 74267450 of 10420 papers

TitleStatusHype
Evolutionary NAS with Gene Expression Programming of Cellular EncodingCode0
On the Difficulty of Membership Inference AttacksCode1
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing FlowsCode0
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference MeasureCode1
An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress DetectionCode0
Accelerating Neural Network Inference by Overflow Aware Quantization0
Generative Adversarial Networks for Bitcoin Data Augmentation0
SCAN: Learning to Classify Images without LabelsCode2
Adaptive Adversarial Logits Pairing0
An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnosticsCode0
Hyperspectral Image Classification with Attention Aided CNNsCode1
Networks with pixels embedding: a method to improve noise resistance in images classificationCode0
Peri-Net-Pro: The neural processes with quantified uncertainty for crack patterns0
One of these (Few) Things is Not Like the Others0
SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation0
Semi-supervised Medical Image Classification with Global Latent MixingCode1
From ImageNet to Image Classification: Contextualizing Progress on BenchmarksCode1
Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image ClassificationCode1
HyperSTAR: Task-Aware Hyperparameters for Deep Networks0
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud0
Perceptual Hashing applied to Tor domains recognition0
Reducing Overlearning through Disentangled Representations by Suppressing Unknown TasksCode0
On Intrinsic Dataset Properties for Adversarial Machine LearningCode1
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural NetworksCode0
Improved Noisy Student Training for Automatic Speech RecognitionCode1
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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