Image Classification
3718 papers with code • 165 benchmarks • 238 datasets
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.
Libraries
Use these libraries to find Image Classification models and implementationsDatasets
Subtasks
- Out of Distribution (OOD) Detection
- Few-Shot Image Classification
- Fine-Grained Image Classification
- Semi-Supervised Image Classification
- Semi-Supervised Image Classification
- Learning with noisy labels
- Hyperspectral Image Classification
- Self-Supervised Image Classification
- Small Data Image Classification
- Multi-Label Image Classification
- Genre classification
- Sequential Image Classification
- Unsupervised Image Classification
- Document Image Classification
- Satellite Image Classification
- Sparse Representation-based Classification
- Photo geolocation estimation
- Image Classification with Differential Privacy
- Superpixel Image Classification
- Classification Consistency
- Gallbladder Cancer Detection
- Artistic style classification
- Artist classification
- Temporal Metadata Manipulation Detection
- Misclassification Rate - Natural Adversarial Samples
- Scale Generalisation
Latest papers with no code
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks
Adversarial attacks have demonstrated the vulnerability of Machine Learning (ML) image classifiers in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems.
Multi-scale Unified Network for Image Classification
Convolutional Neural Networks (CNNs) have advanced significantly in visual representation learning and recognition.
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification
Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data.
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer.
On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition
While this wealth of information can be challenging to analyze using traditional methods, ML provides a seamless approach to this task.
Multi-Task Learning with Multi-Task Optimization
Multi-task learning solves multiple correlated tasks.
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis
Exploring the application of deep learning technologies in the field of medical diagnostics, Magnetic Resonance Imaging (MRI) provides a unique perspective for observing and diagnosing complex neurodegenerative diseases such as Alzheimer Disease (AD).
A Deep Learning Architectures for Kidney Disease Classification
Deep learning has become an extremely powerful tool for complex tasks such as image classification and segmentation.
Image Classification with Rotation-Invariant Variational Quantum Circuits
Variational quantum algorithms are gaining attention as an early application of Noisy Intermediate-Scale Quantum (NISQ) devices.
Your Image is My Video: Reshaping the Receptive Field via Image-To-Video Differentiable AutoAugmentation and Fusion
The landscape of deep learning research is moving towards innovative strategies to harness the true potential of data.