Natural Language Processing
 
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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.

Source: Metamorphic Testing for Object Detection Systems

Libraries

Use these libraries to find Image Classification models and implementations
rwightman/pytorch-image-models
63 papers
19,244
osmr/imgclsmob
50 papers
2,584
PaddlePaddle/PaddleClas
48 papers
4,099
pytorch/vision
26 papers
11,914
See all 27 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

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.

397
Paper
Code

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.

270
Paper
Code

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.

136
Paper
Code

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.

130
Paper
Code

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.

129
Paper
Code

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.

120
Paper
Code

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.

113
Paper
Code

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.

110
Paper
Code

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.

105
Paper
Code

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.

102
Paper
Code
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