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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of deep learning image classification papers and codes
| Date | Stars |
|---|---|
| 2026-07-24 | 3056 |
| 2026-07-25 | 3056 |
| 2026-07-28 | 3058 |
| 2026-07-30 | 3058 |
| 2026-07-31 | 3058 |
| 2026-08-06 | 3060 |
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# Awesome - Image Classification [](https://github.com/sindresorhus/awesome) A curated list of deep learning image classification papers and codes since 2014, Inspired by [awesome-object-detection](https://github.com/amusi/awesome-object-detection), [deep_learning_object_detection](https://github.com/hoya012/deep_learning_object_detection) and [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers). ## Background 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](https://github.com/hoya012/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](https://github.com/weiaicunzai/pytorch-cifar100).** ## Performance Table 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 - Published In: which conference or journal the paper was published in. | ConvNet | ImageNet top1 acc | ImageNet top5 acc | Published In | |:--------------------------:|:-----------------:|:-----------------:|:------------------:| | Vgg | 76.3 | 93.2 | ICLR2015 | | GoogleNet | - | 93.33 | CVPR2015 | | PReLU-nets | - | 95.06 | ICCV2015 | | ResNet | - | 96.43 | CVPR2015 | | PreActResNet | 79.9 | 95.2 | CVPR2016 | | Inceptionv3 | 82.8 | 96.42 | CVPR2016 | | Inceptionv4 | 82.3 | 96.2 | AAAI2016 | | Inception-ResNet-v2 | 82.4 | 96.3 | AAAI2016 | |Inceptionv4 + Inception-ResNet-v2| 83.5 | 96.92 | AAAI2016 | | RiR | - | - | ICLR Workshop2016 | | Stochastic Depth ResNet | 78.02 | - | ECCV2016 | | WRN | 78.1 | 94.21 | BMVC2016 | | SqueezeNet | 60.4 | 82.5 | arXiv2017([rejected by ICLR2017](https://openreview.net/forum?id=S1xh5sYgx)) | | GeNet | 72.13 | 90.26 | ICCV2017 | | MetaQNN | - | - | ICLR2017 | | PyramidNet | 80.8 | 95.3 | CVPR
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Beijing University of Posts and Telecommunications · China
56
jonkoi · eSmartSystems · Norway
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3951d0b5182b6726, topic:awesome, topic:awesome-list, topic:papers
matched fp:3951d0b5182b6726, topic:computer-vision, topic:image-classification, readme:computer vision