Image Classification

3468 papers with code • 161 benchmarks • 232 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.

Source: Metamorphic Testing for Object Detection Systems


Use these libraries to find Image Classification models and implementations

Most implemented papers

Deep Residual Learning for Image Recognition

tensorflow/models CVPR 2016

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

tensorflow/models 4 Sep 2014

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

tensorflow/tensorflow 17 Apr 2017

We present a class of efficient models called MobileNets for mobile and embedded vision applications.

MobileNetV2: Inverted Residuals and Linear Bottlenecks

tensorflow/models CVPR 2018

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.

Densely Connected Convolutional Networks

liuzhuang13/DenseNet CVPR 2017

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.

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

google-research/vision_transformer ICLR 2021

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

tensorflow/tpu ICML 2019

Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

AlexeyAB/darknet 27 Nov 2019

Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

ramprs/grad-cam ICCV 2017

For captioning and VQA, we show that even non-attention based models can localize inputs.

Rethinking the Inception Architecture for Computer Vision

tensorflow/models CVPR 2016

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks.