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+*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- visual-question-answering
+language:
+- en
+size_categories:
+- 1K<n<10K
+dataset_info:
+  features:
+  - name: image_index
+    dtype: string
+  - name: image
+    dtype: image
+  - name: q_index
+    dtype: int64
+  - name: question
+    dtype: string
+  - name: answer
+    dtype: string
+  - name: answer_type
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 246230145.0
+    num_examples: 501
+  download_size: 106490728
+  dataset_size: 246230145.0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+---
+# Omni3D-Bench
+This repository contains the Omni3D-Bench dataset introduced in the paper [Visual Agentic AI for Spatial Reasoning with a Dynamic API]() Omni3D-Bench contains 500 challenging (image, question, answer) tuples of diverse, real-world scenes sourced from [Omni3D](https://github.com/facebookresearch/omni3d) for complex 3D spatial reasoning.
+
+View samples from the dataset [here](https://glab-caltech.github.io/vadar/omni3d-bench.html).
+
+The dataset is released under the [Creative Commons Non-Commercial](https://creativecommons.org/licenses/by-nc/4.0/deed.en) license. 
+
+## Usage
+
+The benchmark can be accessed with the following code:
+
+```
+from datasets import load_dataset
+dataset = load_dataset("dmarsili/Omni3D-Bench")
+```
+
+We additionally provide a `.zip` file including all the images and annotations.
+
+## Annotations
+
+Samples in Omni3D-Bench consist of images, questions, and ground-truth answers. Samples can be loaded as python dictonaries in the following format:
+
+```
+<!-- annotations.json -->
+{
+    "questions": [
+        {
+            "image_index"               : str, image ID
+            "question_index"            : str, question ID
+            "image"                     : PIL Image, image for query
+            "question"                  : str, query
+            "answer_type"               : str, expected answer type - {int, float, str}
+            "answer"                    : str|int|float, ground truth response to the query
+        },
+        {
+            ...
+        },
+        ...
+    ]
+}
+```
+
+## Citation
+
+If you use the Omni3D-Bench dataset in your research, please use the following BibTeX entry.
+
+```bibtex
+
+```

data/train-00000-of-00001.parquet

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omni3d-bench.zip

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