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 276300 of 10419 papers

TitleStatusHype
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image ClassificationCode2
Decoupled Knowledge DistillationCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
A Simple Framework for Contrastive Learning of Visual RepresentationsCode2
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural NetworksCode2
Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-TuningCode2
Effective Data Augmentation With Diffusion ModelsCode2
An Overview of Deep Semi-Supervised LearningCode2
Context Encoding for Semantic SegmentationCode2
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense PredictionCode2
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text LabelsCode2
Transferability of Adversarial Examples to Attack Cloud-based Image Classifier ServiceCode2
Continual Forgetting for Pre-trained Vision ModelsCode2
FasterViT: Fast Vision Transformers with Hierarchical AttentionCode2
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed GradientsCode2
Fixing the train-test resolution discrepancyCode2
Fixing the train-test resolution discrepancy: FixEfficientNetCode2
MobileOne: An Improved One millisecond Mobile BackboneCode2
AdaFisher: Adaptive Second Order Optimization via Fisher InformationCode2
CLIP-Art: Contrastive Pre-training for Fine-Grained Art ClassificationCode2
A Survey on Mixup Augmentations and BeyondCode2
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
Generalized Parametric Contrastive LearningCode2
GIT: A Generative Image-to-text Transformer for Vision and LanguageCode2
Adapter is All You Need for Tuning Visual TasksCode2
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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