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 2650 of 10419 papers

TitleStatusHype
Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like ArchitecturesCode4
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision ApplicationsCode4
Catastrophic Forgetting in Deep Learning: A Comprehensive TaxonomyCode4
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment AnythingCode4
Efficient Post-training Quantization with FP8 FormatsCode4
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One DayCode4
InceptionNeXt: When Inception Meets ConvNeXtCode4
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and VideoCode4
AltCLIP: Altering the Language Encoder in CLIP for Extended Language CapabilitiesCode4
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable ConvolutionsCode4
Benchopt: Reproducible, efficient and collaborative optimization benchmarksCode4
Vision GNN: An Image is Worth Graph of NodesCode4
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense PredictionCode4
Architecture-Agnostic Masked Image Modeling -- From ViT back to CNNCode4
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual ModelsCode4
Visual Attention NetworkCode4
Detectron2 Object Detection & Manipulating Images using CartoonizationCode4
RegNet: Self-Regulated Network for Image ClassificationCode4
A Framework For Contrastive Self-Supervised Learning And Designing A New ApproachCode4
Deep Residual Learning for Image RecognitionCode4
Falcon: A Remote Sensing Vision-Language Foundation ModelCode3
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMsCode3
ADOPT: Modified Adam Can Converge with Any β_2 with the Optimal RateCode3
Cascade Prompt Learning for Vision-Language Model AdaptationCode3
SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation ModelsCode3
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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