About

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

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Datasets

Greatest papers with code

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

17 Apr 2017tensorflow/tensorflow

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

IMAGE CLASSIFICATION OBJECT DETECTION

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

ECCV 2018 tensorflow/models

The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.

IMAGE CLASSIFICATION LESION SEGMENTATION SEMANTIC SEGMENTATION

MobileNetV2: Inverted Residuals and Linear Bottlenecks

CVPR 2018 tensorflow/models

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.

IMAGE CLASSIFICATION OBJECT DETECTION PERSON RE-IDENTIFICATION RETINAL OCT DISEASE CLASSIFICATION SEMANTIC SEGMENTATION

Progressive Neural Architecture Search

ECCV 2018 tensorflow/models

We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.

IMAGE CLASSIFICATION NEURAL ARCHITECTURE SEARCH

Xception: Deep Learning with Depthwise Separable Convolutions

CVPR 2017 tensorflow/models

We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution).

IMAGE CLASSIFICATION

Deep Residual Learning for Image Recognition

CVPR 2016 tensorflow/models

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.

DOMAIN GENERALIZATION FINE-GRAINED IMAGE CLASSIFICATION IMAGE-TO-IMAGE TRANSLATION OBJECT DETECTION PERSON RE-IDENTIFICATION RETINAL OCT DISEASE CLASSIFICATION SEMANTIC SEGMENTATION

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

11 Feb 2015tensorflow/models

Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change.

IMAGE CLASSIFICATION

Learning Transferable Architectures for Scalable Image Recognition

CVPR 2018 tensorflow/models

In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named "NASNet architecture".

IMAGE CLASSIFICATION NEURAL ARCHITECTURE SEARCH

Identity Mappings in Deep Residual Networks

16 Mar 2016tensorflow/models

Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors.

IMAGE CLASSIFICATION