README.md

---
license: other
license_name: custom-apple-license
license_link: https://github.com/apple/ml-mobileclip/blob/main/LICENSE
viewer: false
task_categories:
  - text-to-image
  - image-to-text
language:
  - en
library_name: tic-clip
---

# Dataset Card for TiC-DataComp

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This dataset containts metadata for TiC-DataComp benchmark for time-continual learning of image-text models.
The dataset containts timestamp information for DataComp-1B in the form of UIDs groupings by year/month sourced from the original CommonCrawl.
We also release UIDs for our TiC-DataCompNet and TiC-DataComp-Retrieval evaluations for continual learning of CLIP models.
For details on how to use the metadata, please visit our [github repository](https://github.com/apple/ml-tic-clip).

## Dataset Details

### Dataset Description

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Keeping large foundation models up to date on latest data is inherently expensive.
To avoid the prohibitive costs of constantly retraining, it is imperative to continually train these models.
This problem is exacerbated by the lack of any large scale continual learning benchmarks or baselines.
We introduce the first set of web-scale Time-Continual (TiC) benchmarks for training vision-language models:
TiC-DataComp, TiC-YFCC, and TiC-Redcaps. TiC-DataComp, our largest dataset,
contains over 12.7B timestamped image-text pairs spanning 9 years (2014-2022).
We first use our benchmarks to curate various dynamic evaluations to measure temporal robustness of existing models.
We show OpenAI's CLIP (trained on data up to 2020) loses ≈8% zero-shot accuracy on our curated retrieval task from 2021-2022 compared with more recently trained models in OpenCLIP repository.
We then study how to efficiently train models on time-continuous data.
We demonstrate that a simple rehearsal-based approach that continues training from the last checkpoint and replays old data reduces compute by 2.5× when compared to the standard practice of retraining from scratch.
Code is available at [this https URL](https://github.com/apple/ml-tic-clip).


- **Developed by:** Apple
- **License:** See [LICENSE](https://github.com/apple/ml-tic-clip/blob/main/LICENSE)

## Uses

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Researchers can use TiC-DataComp dataset to design and evaluate continual learning methods at large-scale for image-text models.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

```
- tic-datacomp_training_monthly/<YYYMM>.npy
  - List of UIDs for each month.
- tic-datacomp_training_yearly_noeval/<YYY>.npy
  - List of UIDs for each year after removing yearly evaluation sets.
- tic-datacomp_retrieval_evals_year2uids: TiC-DataComp-Retrieval evaluation UIDs per year.
- tic-datacompnet_year2uids: TiC-DataCompNet evaluation UIDs per year.
```

## Citation

**[TiC-CLIP: Continual Training of CLIP Models](https://arxiv.org/abs/2310.16226). (ICLR 2024)**
*Garg, S., Farajtabar, M., Pouransari, H., Vemulapalli, R., Mehta, S., Tuzel, O., Shankar, V. and Faghri, F..*

```bibtex
@inproceedings{garg2024tic,
  title={TiC-CLIP: Continual Training of CLIP Models},
  author={Garg, Saurabh and Farajtabar, Mehrdad and Pouransari, Hadi and Vemulapalli, Raviteja and Mehta, Sachin and Tuzel, Oncel and Shankar, Vaishaal and Faghri, Fartash},
  booktitle={The Twelfth International Conference on Learning Representations (ICLR)},
  year={2024},
  url={https://openreview.net/forum?id=TLADT8Wrhn}
}