| | --- |
| | task_categories: |
| | - zero-shot-classification |
| | size_categories: |
| | - 1M<n<10M |
| | --- |
| | |
| | This repository hosts the JANuS (Joint Annotations and Names) dataset introduced in the 2023 paper Distributionally Robust Classification on a Data Budget. |
| |
|
| | As of this writing, ours is the only public dataset which is both fully annotated with ground-truth labels and fully captioned with web-scraped captions. |
| |
|
| | It is designed to be used for controlled experiments with vision-language models. |
| |
|
| | ## What is in JANuS?[](#what-is-in-JANuS) |
| |
|
| | JANuS provides metadata and image links for four new training datasets; all of these datasets are designed for evaluation on a subset of 100 classes chosen from ImageNet-1000. |
| |
|
| | Each dataset in JANuS is either a subset or a superset of an existing dataset, and each is fully captioned and fully labeled, either using annotated or synthetic labels. |
| |
|
| | For additional details on our methodology for gathering JANuS, as well as explanations of terms like "subset matching", please refer to our paper. |
| |
|
| | 1. **ImageNet-100:** A superset of ImageNet with over 50,000 newly |
| | annotated samples, including flickr-captions and blip-captions. |
| | |
| | 2. **OpenImages-100:** A subset of OpenImages with new mappings from |
| | OpenImages to ImageNet classes, restored original flickr-captions, |
| | and new BLIP-captions. |
| | |
| | 3. **LAION-100:** A subset of LAION-15m with samples selected via |
| | subset matching. |
| | |
| | 4. **YFCC-100:** A subset of YFCC-15m with samples selected via subset |
| | matching. |
| | |
| | ## Training on JANuS[](#training-on-JANuS) |
| |
|
| | JANuS is designed to allow researchers to easily compare the effects of different labeling strategies on model performance. As such, every subset of JANuS includes at least two labeling sources. |
| |
|
| | * **idx** labels are integers, mapping to [ImageNet-1k class labels](https://deeplearning.cms.waikato.ac.nz/user-guide/class-maps/IMAGENET/) |
| | * **caption** labels are natural language captions (usually in English), and are suitable for training VL-loss models like [CLIP](https://openai.com/blog/clip/) |
| |
|
| | For YFCC-100 and LAION-100, the idx labels are synthetic, and are generated via a simple subset matching strategy. For ImageNet-100 and OpenImages-100, the idx labels are annotated by humans. |
| |
|
| | YFCC-100, ImageNet-100 and OpenImages-100 contain captions sourced from Flickr. LAION-100 contains captions sourced from alt-text descriptions. |
| |
|
| | Additional labeling sources are available for some of the datasets in JANuS; please reference our paper for a reference key for all of the columns in the spreadsheets. |
| |
|
| | [VL Hub](https://github.com/penfever/vlhub/), a framework for vision language model training, can be used to reproduce the experiments in our paper. |
| |
|
| | ## Evaluation on JANuS[](#evaluation-on-JANuS) |
| |
|
| | Evaluation methods for JANuS models are the same as those for ImageNet models, except that we evaluate only on a subset of all ImageNet classes. |
| |
|
| | For details on which classes are included in JANuS, please see metadata/in100_classes.txt in this repo. |
| | |
| | ## Citations |
| | |
| | If you find our dataset useful, please cite our paper -- |
| | |
| | ``` |
| | @article{ |
| | feuer2023distributionally, |
| | title={Distributionally Robust Classification on a Data Budget}, |
| | author={Benjamin Feuer and Ameya Joshi and Minh Pham and Chinmay Hegde}, |
| | journal={Transactions on Machine Learning Research}, |
| | issn={2835-8856}, |
| | year={2023}, |
| | url={https://openreview.net/forum?id=D5Z2E8CNsD}, |
| | note={} |
| | } |
| | ``` |
