Datasetpublic

marco-schouten/hidden-objects

Hidden-Objects Image-object pairs with localized bounding boxes for learning realistic object placement in background scenes. Project page: https://hidden-objects.github.io/ Backgrounds: Places365 Schema Field Type Description entry_id int64 Unique row identifier bg_path string Relative path to background image in Places365 fg_class string Foreground object category (e.g. "bottle") bbox list Normalized bounding box [x, y, w, h] in range 0–1… See the full description on the dataset page: https://huggingface.co/datasets/marco-schouten/hidden-objects.

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Hidden-Objects

Image-object pairs with localized bounding boxes for learning realistic object placement in background scenes.

—Project page: https://hidden-objects.github.io/
—Backgrounds: Places365

Schema

Field	Type	Description
`entry_id`	int64	Unique row identifier
`bg_path`	string	Relative path to background image in Places365
`fg_class`	string	Foreground object category (e.g. `"bottle"`)
`bbox`	list	Normalized bounding box `[x, y, w, h]` in range 0–1
`label`	int64	1 = positive, 0 = negative
`image_reward_score`	float64	ImageReward quality score
`confidence`	float64	GroundingDINO detection confidence
`source`	string	Origin tag of the annotation

Sample:

json

{
  "entry_id": 1,
  "bg_path": "data_large_standard/k/kitchen/00002986.jpg",
  "fg_class": "bottle",
  "bbox": [0.542969, 0.591797, 0.0625, 0.152344],
  "label": 1,
  "image_reward_score": -1.542461,
  "confidence": 0.388181,
  "source": "ho"
}

Bounding Boxes

Bounding boxes are relative to a 512×512 center crop of the background image:

python

# Normalized → pixel coordinates
x, y, w, h = [v * 512 for v in bbox]

Usage

Quick start

python

from datasets import load_dataset

dataset = load_dataset("marco-schouten/hidden-objects")
print(dataset["train"][0])

PyTorch Dataset

Requires Places365 backgrounds downloaded locally:

bash

huggingface-cli login

python

import torchvision.datasets as datasets

background_images = datasets.Places365(root="./data/places365", split="train-standard", small=False, download=True)

python

import os
import torch
from PIL import Image
from torch.utils.data import Dataset
from datasets import load_dataset
import torchvision.transforms as T

class HiddenObjectsDataset(Dataset):
    def __init__(self, places_root, split="train"):
        self.data = load_dataset("marco-schouten/hidden-objects", split=split)
        self.places_root = places_root
        self.transform = T.Compose([T.Resize(512), T.CenterCrop(512), T.ToTensor()])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        item = self.data[idx]
        image = self.transform(Image.open(os.path.join(self.places_root, item["bg_path"])).convert("RGB"))
        return {
            "entry_id": item["entry_id"],
            "image": image,
            "bbox": torch.tensor(item["bbox"]) * 512,
            "label": item["label"],
            "class": item["fg_class"],
            "image_reward_score": item["image_reward_score"],
            "confidence": item["confidence"],
        }

# Usage
hidden_object_dataset = HiddenObjectsDataset(places_root="./data/places365")