---
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: 27536486.0
    num_examples: 300
  - name: test
    num_bytes: 1057911404.0
    num_examples: 10888
  - name: test_dense
    num_bytes: 142883772.0
    num_examples: 1200
  download_size: 1168229577
  dataset_size: 1228331662.0
---
# PODS: Personal Object Discrimination Suite
<h3 align="center"><a href="https://personalized-rep.github.io" style="color: #2088FF;">🌐Project page</a>&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp
  <a href="https://example2.com" style="color: #2088FF;">📖Paper</a>&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp
<a href="#citation" style="color: #2088FF;">GitHub</a><br></h3>

We introduce the PODS (Personal Object Discrimination Suite) dataset, a new benchmark for personalized vision tasks. 
<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/65f9d4100f717eb3e67556df/I6COn1U2CmzZFFs998JsL.jpeg" alt="pods.jpg" />
</p>

## 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.
* 12 test images per instance (3 per split).

PODS is [available on HuggingFace](#hf-link-here), or can be directly downloaded [here](#link-here).

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]`)