Create README.md

README.md

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+---
+license: other
+license_name: custom-apple-license
+license_link: https://github.com/apple/ml-mobileclip/blob/main/LICENSE
+dataset_info: NULL
+task_categories:
+  - text-to-image
+  - image-to-text
+language:
+  - en
+---
+
+# Dataset Card for TiC-DataComp
+
+<!-- Provide a quick summary of the dataset. -->
+
+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
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+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
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+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}
+}