| --- |
| license: apache-2.0 |
| task_categories: |
| - image-classification |
| --- |
| |
| # OmniBenchmark-1K |
|
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| OmniBenchmark-1K is a challenging benchmark for Class-Incremental Continual Learning designed to evaluate performance on very long task sequences, ranging from 100 to over 300 non-overlapping tasks. |
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| The dataset was introduced in the paper [Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts](https://huggingface.co/papers/2602.03473). |
|
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| - **GitHub:** [https://github.com/LMMMEng/CaRE](https://github.com/LMMMEng/CaRE) |
| - **Paper:** [Hugging Face](https://huggingface.co/papers/2602.03473) | [arXiv](https://arxiv.org/abs/2602.03473) |
|
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| ## Description |
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| OmniBenchmark-1K provides a large-scale evaluation protocol for comprehensively assessing continual learners. While standard benchmarks often focus on 5-20 tasks, this dataset allows for performance evaluation on extremely long sequences, testing the stability and plasticity of models over time. |
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| ## Citations |
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| If you find this dataset useful for your research, please cite: |
|
|
| ```bibtex |
| @inproceedings{lou2026scaling, |
| title={Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts}, |
| author={Lou, Meng and Fu, Yunxiang and Yu, Yizhou}, |
| booktitle={International Conference on Machine Learning}, |
| year={2026}, |
| } |
| |
| @inproceedings{zhang2022benchmarking, |
| title={Benchmarking omni-vision representation through the lens of visual realms}, |
| author={Zhang, Yuanhan and Yin, Zhenfei and Shao, Jing and Liu, Ziwei}, |
| booktitle={European Conference on Computer Vision}, |
| year={2022}, |
| } |
| ``` |
