PODS: Personal Object Discrimination Suite

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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},
}