Image Classification
4312 papers with code • 166 benchmarks • 263 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
- Learning with noisy labels
- Learning with noisy labels
- Semi-Supervised Image Classification
- Hyperspectral Image Classification
- Self-Supervised Image Classification
- Multi-Label Image Classification
- Small Data Image Classification
- Genre classification
- Sequential Image Classification
- Unsupervised Image Classification
- Efficient ViTs
- Document Image Classification
- Multi-Label Image Recognition
- Token Reduction
- Satellite Image Classification
- Photo geolocation estimation
- Sparse Representation-based Classification
- Image Classification with Differential Privacy
- Classification Consistency
- Superpixel Image Classification
- Gallbladder Cancer Detection
- Artistic style classification
- Artist classification
- Temporal Metadata Manipulation Detection
- Misclassification Rate - Natural Adversarial Samples
- Concept-based Classification
- Scale Generalisation
- Raw vs Ripe (Generic)
- Fruit-type + Maturity-state Prediction (Multi-label Classifivation)
- Fruit-type + Maturity-state Prediction (Multi-label Classification)
- Railway Track Image Classification
Most implemented papers
Deep Residual Learning for Image Recognition
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Very Deep Convolutional Networks for Large-Scale Image Recognition
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
Densely Connected Convolutional Networks
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
For captioning and VQA, we show that even non-attention based models can localize inputs.
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.