The CLEAR Benchmark: Continual LEArning on Real-World Imagery

By Carnegie Mellon University and CMU Argo AI Center

CLEAR is a novel continual/lifelong benchmark that captures real-world distribution shifts in Internet image collection (YFCC100M) from 2004 to 2014.

For long, researchers in continual learning (CL) community have been working with artificial CL benchmarks such as "Permuted-MNIST" and "Split-CIFAR", which do not align with practical applications. In reality, distribution shifts are smooth, such as natural temporal evolution of visual concepts.

Below are examples of classes in CLEAR-100 that changed over the past decade:

About CLEAR Benchmark

The CLEAR Benchmark and the CLEAR-10 dataset are first introduced in our NeurIPS 2021 paper.

In spirit of the famous CIFAR-10/CIFAR-100 benchmarks for static image classification tasks, we also collected a more challenging CLEAR-100 with a diverse set of 100 classes.

We hope our CLEAR-10/-100 benchmarks can be the new "CIFAR" as a test stone for continual/lifelong learning community.

We are also extending CLEAR to an ImageNet-scale benchmark. If you have feedback and insights, feel free to reach out to us!

pageAbout us

Please cite our paper if it is useful for your research:

  title={The CLEAR Benchmark: Continual LEArning on Real-World Imagery},
  author={Lin, Zhiqiu and Shi, Jia and Pathak, Deepak and Ramanan, Deva},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},

1st CLEAR challenge on CVPR 2022

In June 2022, the 1st CLEAR Challenge was hosted on CVPR 2022 Open World Vision Workshop, with a total of 15 teams from 21 different countries and regions partcipating. You may find a quick summary of the workshop in the below page:

🚀page1st CLEAR Challenge (CVPR'22)

Given the top teams' promising performance on CLEAR-10/-100 benchmarks via utilizing methods that improve generalization, such as sharpness aware minimization, supervised contrastive loss, strong data augmentation, experience replay, etc., we believe there are still a wealth of problems in CLEAR for the community to explore, such as:

  • Improving Forward Transfer and Next-Domain Accuracy

  • Unsupervised/Online Domain Generalization

  • Self-supervised/Semi-Supervised Continuous Learning

In the following pages, we will explain the motivation of CLEAR benchmark, how it is curated via visio-linguistic approach, its evaluation protocols, and a walk-through of the 1st CLEAR Challenge on CVPR'22.

You can also jump to the links for downloading CLEAR dataset:

pageDownload CLEAR-10/CLEAR-100

Last updated