metadata

license: mit
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: test_dense
        path: data/test_dense-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: mask
      dtype: image
    - name: label
      dtype: string
    - name: scene_type
      dtype: string
  splits:
    - name: train
      num_bytes: 27536486
      num_examples: 300
    - name: test
      num_bytes: 1057988392
      num_examples: 10888
    - name: test_dense
      num_bytes: 142893072
      num_examples: 1200
  download_size: 1168238221
  dataset_size: 1228417950
tags:
  - personalization
  - instance_detection
  - instance_classification
  - instance_segmentation
  - instance_retrieval

PODS: Personal Object Discrimination Suite

🌐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.

PODS is split class-wise into a validation set (6 classes per semantic category) and a test set (14 classes per semantic category). All test performance reported in our paper is from the test set of classes.

Within each class, images are divided into a train/retrieval set (3 images) and a test/query set. The test/query set is then further divided into 4 test splits reflecting different distribution shifts.

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 a dictionary:
- class: The class name that the image belongs to
- split: One of [train, test] indicating if the image is in the train or test set for that class.
- test_split: For images in the test split, denotes which distribution-shift test split the image is in: One of [in_distribution, pose, distractors, pose_and_distractors]

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},
}