Image Classification
State-of-the-art leaderboards
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Subtasks
Greatest papers with code
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
NeurIPS 2018
• tensorflow/models
• 
Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.
SOTA for Semantic Segmentation on Cityscapes (using extra training data)
IMAGE CLASSIFICATION META-LEARNING SEMANTIC SEGMENTATION STREET SCENE PARSING
AutoAugment: Learning Augmentation Policies from Data
In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.
SOTA for Image Classification on SVHN
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.
#30 best model for
Image Classification
on ImageNet
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".
#7 best model for
Image Classification
on ImageNet
The iNaturalist Species Classification and Detection Dataset
CVPR 2018
• tensorflow/models
•
Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories.
SOTA for Image Classification on iNaturalist
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
#32 best model for
Image Classification
on ImageNet
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).
#19 best model for
Image Classification
on ImageNet
Wide Residual Networks
Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance.
#5 best model for
Image Classification
on SVHN
Identity Mappings in Deep Residual Networks
Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors.
#15 best model for
Image Classification
on CIFAR-100
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network.
#11 best model for
Image Classification
on ImageNet