--- license: mit size_categories: - 10K🌐Project page 📖Paper GitHub
We introduce the PODS (Personal Object Discrimination Suite) dataset, a new benchmark for personalized vision tasks.

## PODS The PODS dataset is new a benchmark for personalized vision tasks. It includes: * 100 common household objects from 5 semantic categories * 4 tasks (classification, retrieval, segmentation, detection) * 4 test splits with different distribution shifts. * 71-201 test images per instance with classification label annotations. * 12 test images per instance (3 per split) with segmentation annotations. Metadata is stored in two files: * `pods_info.json`: * `classes`: A list of class names * `class_to_idx`: Mapping of each class to an integer id * `class_to_sc`: Mapping of each class to a broad, single-word semantic category * `class_to_split`: Mapping of each class to the `val` or `test` split. * `pods_image_annos.json`: Maps every image ID to its class and test split (one of `[train, objects, pose, all]`) ## Using PODS ### Loading the dataset using HuggingFace To load the dataset using HuggingFace `datasets`, install the library by `pip install datasets` ``` from datasets import load_dataset pods_dataset = load_dataset("chaenayo/PODS") ``` You can also specify a split by: ``` pods_dataset = load_dataset("chaenayo/PODS", split="train") # or "test" or "test_dense" ``` ### Loading the dataset directly PODS can also be directly downloaded via command: ``` wget https://data.csail.mit.edu/personal_rep/pods.zip ``` ## Citation If you find our dataset useful, please cite our paper: ``` @article{sundaram2024personalized, title = {Personalized Representation from Personalized Generation} author = {Sundaram, Shobhita and Chae, Julia and Tian, Yonglong and Beery, Sara and Isola, Phillip}, journal = {Arxiv}, year = {2024}, } ```