---
title: X-TAIL
canonical_url: "https://www.modelscope.cn/datasets/ForestLuo/X-TAIL"
md_url: "https://www.modelscope.cn/datasets/ForestLuo/X-TAIL.md"
repository: ForestLuo/X-TAIL
last_updated: 2025-08-26
license: "Apache License 2.0"
storage_size: "47 GB"
downloads: 5526
stars: 1
---

# X-TAIL

> X-TAIL - ForestLuo 在 ModelScope 开源的数据集。Introduction of X-TAIL Benchmark

ForestLuo/X-TAIL 是 ModelScope 魔搭社区上的数据集，存储大小 47 GB，采用 Apache License 2.0 许可。

- **Repository**: ForestLuo/X-TAIL
- **License**: Apache License 2.0
- **Storage size**: 47 GB
- **Downloads**: 5526
- **Stars**: 1
- **Last updated**: 2025-08-26

Source: https://www.modelscope.cn/datasets/ForestLuo/X-TAIL

---

#### Introduction of X-TAIL Benchmark

The X-TAIL (Cross-domain Task-Agnostic Incremental Learning) benchmark is composed of 10 datasets: Aircraft, Caltech101, DTD, EuroSAT, Flowers, Food, MNIST, OxfordPet, StanfordCars, and SUN397. It includes a total of 1,100 classes across all tasks.



We invite you to explore our work on the X-TAIL benchmark, published at ICML 2025: [LADA: Scalable Label-Specific CLIP Adapter for Continual Learning](https://icml.cc/virtual/2025/poster/43751). Code is available at: [https://github.com/MaolinLuo/LADA](https://github.com/MaolinLuo/LADA)

#### X-TAIL数据集介绍

X-TAIL(Cross-domain Task-Agnostic Incremental Learning)[1] 数据集由  Aircraft[2], Caltech101[3], DTD[4], EuroSAT[5], Flowers[6], Food[7], MNIST[8], OxfordPet[9], StanfordCars[10], and SUN397[11] 共10个数据集组成。所有任务包括总共 1,100 个类别。



欢迎关注我们在X-TAIL数据集上的发表于ICML2025的工作：[LADA: Scalable Label-Specific CLIP Adapter for Continual Learning](https://icml.cc/virtual/2025/poster/43751). 代码地址：[https://github.com/MaolinLuo/LADA](https://github.com/MaolinLuo/LADA)



#### 下载方法 
:modelscope-code[]{type="sdk"}
:modelscope-code[]{type="git"}

#### 引用

[1] Xu, Y., Chen, Y., Nie, J., Wang, Y., Zhuang, H., and Oku- mura, M. Advancing cross-domain discriminability in continual learning of vision-language models. Advances in neural information processing systems, 2024.

[2] Maji, S., Rahtu, E., Kannala, J., Blaschko, M., and Vedaldi, A. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013.

[3] Fei-Fei, L., Fergus, R., and Perona, P. Learning generative visual models from few training examples: An incremen- tal bayesian approach tested on 101 object categories. In Conference on computer vision and pattern recognition workshop, 2004.

[4] Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A. Describing textures in the wild. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, 2014.

[5] Helber, P., Bischke, B., Dengel, A., and Borth, D. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of selected topics in applied earth observations and remote sensing, 2019.

[6] Nilsback, M.-E. and Zisserman, A. Automated flower clas- sification over a large number of classes. In 2008 Sixth Indian conference on computer vision, graphics & image processing, 2008.

[7] Bossard, L., Guillaumin, M., and Van Gool, L. Food-101– mining discriminative components with random forests. In European conference of computer vision, 2014.

[8] Deng, L. The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE signal processing magazine, 2012.

[9] Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C. Cats and dogs. In 2012 IEEE conference on computer vision and pattern recognition, 2012.

[10] Krause, J., Stark, M., Deng, J., and Fei-Fei, L. 3d object rep- resentations for fine-grained categorization. In Proceed- ings of the IEEE international conference on computer vision workshops, 2013.

[11] Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., and Torralba, A. Sun database: Large-scale scene recognition from abbey to zoo. In IEEE computer society conference on computer vision and pattern recognition, 2010.