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 96519675 of 10420 papers

TitleStatusHype
HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices0
I2CANSAY:Inter-Class Analogical Augmentation and Intra-Class Significance Analysis for Non-Exemplar Online Task-Free Continual Learning0
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification0
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification0
IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification0
I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption0
iCaps: An Interpretable Classifier via Disentangled Capsule Networks0
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning0
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)0
I-CNet: Leveraging Involution and Convolution for Image Classification0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation0
Identification and Recognition of Rice Diseases and Pests Using Convolutional Neural Networks0
Identification of Cervical Pathology using Adversarial Neural Networks0
Identification of Seed Cells in Multispectral Images for GrowCut Segmentation0
Identify Apple Leaf Diseases Using Deep Learning Algorithm0
Identifying Land Patterns from Satellite Imagery in Amazon Rainforest using Deep Learning0
Identifying Misinformation from Website Screenshots0
Identifying Mislabeled Images in Supervised Learning Utilizing Autoencoder0
Identifying regions of interest in whole slide images of renal cell carcinoma0
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning0
If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components0
iFlood: A Stable and Effective Regularizer0
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision0
IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification0
I Know What I Don't Know: Improving Model Cascades Through Confidence Tuning0
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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
10RevCol-HTop 1 Accuracy90Unverified