Image Classification
2403 papers with code • 113 benchmarks • 174 datasets
Image Classification is a fundamental task that attempts to comprehend an entire image as a whole. The goal is to classify the image by assigning it to a specific label. Typically, Image Classification refers to images in which only one object appears and is analyzed. In contrast, object detection involves both classification and localization tasks, and is used to analyze more realistic cases in which multiple objects may exist in an image.
Libraries
Use these libraries to find Image Classification models and implementationsSubtasks
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Knowledge Distillation
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Few-Shot Image Classification
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OOD Detection
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Fine-Grained Image Classification
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Fine-Grained Image Classification
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Semi-Supervised Image Classification
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Learning with noisy labels
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Self-Supervised Image Classification
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Hyperspectral Image Classification
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Small Data Image Classification
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Sequential Image Classification
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Genre classification
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Multi-Label Image Classification
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Unsupervised Image Classification
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Document Image Classification
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Sparse Representation-based Classification
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Satellite Image Classification
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Superpixel Image Classification
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Classification Consistency
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Photo geolocation estimation
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Artistic style classification
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Artist classification
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Temporal Metadata Manipulation Detection
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Gallbladder Cancer Detection
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Scale Generalisation
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.
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.
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.
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.
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.
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.
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.
Rethinking the Inception Architecture for Computer Vision
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks.