maujim/CLEVR_sample_500

CLEVR Sample 500

A small, aligned subset of CLEVR v1.0 for fast end-to-end visual question answering experiments. It contains images and questions for training, validation, and test workflows, plus the scene graphs and labels released for the training and validation splits.

This is a deterministic subset of CLEVR v1.0, not a new or official CLEVR release.

Dataset summary

Split	Images	Questions	Scene graphs	Answers and programs
Train	500	5,000	500	Yes
Validation	107	1,070	107	Yes
Test	107	1,070	Not released	No
Total	714	7,140	607	—

No-install viewer

To browse the sample without installing anything, download or clone the complete dataset repository, then double-click viewer.html. The viewer lets you choose the train, validation, or test split; move to previous or next images; jump to an image index; choose a random image; search question text; show or hide answers; inspect functional programs and scene details; and turn on numbered object overlays when available.

Keep viewer.html beside the repository's images/ directory, including images/train/, images/val/, and images/test/. Opening viewer.html by itself, without these adjacent image folders and their PNG files, will not display images.

The validation and test counts preserve the approximate image ratio of the full dataset: 70,000 training images to 15,000 validation images to 15,000 test images. Scaling that ratio from 500 training images gives 107 images for each evaluation split.

Subset construction

The sample uses a reproducible prefix selection based on the original image_index:

• Train: indices 0 through 499
• Validation: indices 0 through 106
• Test: indices 0 through 106

Questions and scene graphs were filtered by the same indices. Original filenames, image indices, question indices, annotations, answers, and functional programs are unchanged; nothing is renumbered.

Repository structure

.
├── viewer.html                # No-install dataset viewer
├── images
│   ├── train/                  # 500 PNG files
│   ├── val/                    # 107 PNG files
│   └── test/                   # 107 PNG files
├── questions
│   ├── CLEVR_train_questions.json
│   ├── CLEVR_val_questions.json
│   └── CLEVR_test_questions.json
├── scenes
│   ├── CLEVR_train_scenes.json
│   └── CLEVR_val_scenes.json
├── COPYRIGHT.txt
├── LICENSE.txt
└── README.txt                  # Original CLEVR format documentation

Annotation format

Each question file contains an info object and a questions array. Every question includes:

• split
• image_index
• image_filename
• question
• question_index

Training and validation questions additionally include answer, program, and question_family_index. As in the original release, test answers and functional programs are not provided.

Training and validation scene files contain an info object and a scenes array. Each scene describes object attributes, 3D and pixel coordinates, camera-relative directions, and spatial relationships. The original CLEVR release does not provide test scene graphs.

Loading the dataset

Download a Hugging Face repository snapshot, then join annotations to images using image_filename:

import json
from pathlib import Path

from huggingface_hub import snapshot_download
from PIL import Image

root = Path(
    snapshot_download(
        repo_id="YOUR_NAMESPACE/CLEVR_sample_500",
        repo_type="dataset",
    )
)

with (root / "questions" / "CLEVR_train_questions.json").open() as file:
    questions = json.load(file)["questions"]

example = questions[0]
image = Image.open(root / "images" / "train" / example["image_filename"])
print(example["question"], example["answer"], image.size)

Use CLEVR_val_questions.json and images/val for validation. Test examples have questions but no ground-truth answers.

Intended use and limitations

This sample is intended for pipeline development, smoke tests, debugging, and fast iteration. It is too small for reproducing full-dataset CLEVR results or making reliable model comparisons.

Because selection uses the first image indices rather than random or stratified sampling, the subset should not be assumed to preserve the full distribution of object combinations, question families, or answers. Results on this sample are not directly comparable with published results on the complete CLEVR benchmark.

CLEVR is synthetic and measures specific forms of visual and compositional reasoning. It does not represent the visual or linguistic diversity of real-world applications.

Source, license, and citation

The files are derived from the official CLEVR v1.0 dataset. The dataset metadata and included license identify the license as Creative Commons Attribution 4.0 International. See LICENSE.txt and COPYRIGHT.txt for the bundled terms and attribution notice.

If you use this subset, cite the original CLEVR paper:

@inproceedings{johnson2017clevr,
  title={CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning},
  author={Johnson, Justin and Hariharan, Bharath and van der Maaten, Laurens and Fei-Fei, Li and Zitnick, C. Lawrence and Girshick, Ross},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2017}
}