Update README.md

---
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.
* 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},
}
```

README.md

@@ -51,11 +51,6 @@ The PODS dataset is new a benchmark for personalized vision tasks. It includes:
 * 71-201 test images per instance with classification label annotations.
 * 12 test images per instance (3 per split) with segmentation annotations.
 
-PODS can be directly downloaded via command:
-```
-wget https://data.csail.mit.edu/personal_rep/pods.zip
-```
-
 Metadata is stored in two files:
 * `pods_info.json`:
   * `classes`: A list of class names
@@ -63,3 +58,26 @@ Metadata is stored in two files:
   * `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
+```