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4134 papers with code • 152 benchmarks • 254 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.
Feb 26, 2019 · 212 papers with code • 88 benchmarks • 23 datasets. Few-Shot Image Classification is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically < 6 examples).
**Medical Image Classification** is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases.
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A curated list of deep learning image classification papers and codes since 2014, Inspired by awesome-object-detection, deep_learning_object_detection and awesome-deep-learning-papers.
I believe image classification is a great start point before diving into other computer vision fields, espacially for begginers who know nothing about deep learning. When I started to learn computer vision, I've made a lot of mistakes, I wish someone could have told me that which paper I should start with back then. There doesn't seem to have a repository to have a list of image classification papers like deep_learning_object_detection until now. Therefore, I decided to make a repository of a list of deep learning image classification papers and codes to help others. My personal advice for people who know nothing about deep learning, try to start with vgg, then googlenet, resnet, feel free to continue reading other listed papers or switch to other fields after you are finished.
Note: I also have a repository of pytorch implementation of some of the image classification networks, you can check out here.
For simplicity reason, I only listed the best top1 and top5 accuracy on ImageNet from the papers. Note that this does not necessarily mean one network is better than another when the acc is higher, cause some networks are focused on reducing the model complexity instead of improving accuracy, or some papers only give the single crop results on ImageNet, but others give the model fusion or multicrop results.
•ConvNet: name of the covolution network
•ImageNet top1 acc: best top1 accuracy on ImageNet from the Paper
•ImageNet top5 acc: best top5 accuracy on ImageNet from the Paper
ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun •pdf: https://arxiv.org/abs/1707.01083 •code: unofficial-tensorflow : https://github.com/MG2033/ShuffleNet •code: unofficial-pytorch : https://github.com/jaxony/ShuffleNet •code: unofficial-caffe : https://github.com/farmingyard/ShuffleNet •code: unofficial-keras : https://github.com/scheckmedia/keras-shufflenet
CondenseNet
CondenseNet: An Efficient DenseNet using Learned Group Convolutions Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger •pdf: https://arxiv.org/abs/1711.09224 •code: official : https://github.com/ShichenLiu/CondenseNet •code: unofficial-tensorflow : https://github.com/markdtw/condensenet-tensorflow
MobileNetV2
MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen •pdf: https://arxiv.org/abs/1801.04381 •code: unofficial-keras : https://github.com/xiaochus/MobileNetV2 •code: unofficial-pytorch : https://github.com/Randl/MobileNetV2-pytorch •code: unofficial-tensorflow : https://github.com/neuleaf/MobileNetV2
Fine-Grained Image Classification. 187 papers with code • 35 benchmarks • 36 datasets. Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers.
30 papers with code • 7 benchmarks • 6 datasets. Models that learn to label each image (i.e. cluster the dataset into its ground truth classes) without seeing the ground truth labels.
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May 11, 2021 · View a PDF of the paper titled Image Classification with Classic and Deep Learning Techniques, by \`Oscar Lorente and 2 other authors. To classify images based on their content is one of the most studied topics in the field of computer vision.
