release

README.md

@@ -27,19 +27,16 @@ This repository contains the visual spatial intelligence benchmark (VSI-Bench),
 
 
 ## Files
-The `test-00000-of-00001.parquet` contains the full dataset annotations and images pre-loaded for processing with HF Datasets. It can be loaded as follows:
+The `test-00000-of-00001.parquet` file contains the complete dataset annotations and pre-loaded images, ready for processing with HF Datasets. It can be loaded using the following code:
 
-<!-- @shusheng -->
 ```python
 from datasets import load_dataset
 vsi_bench = load_dataset("nyu-visionx/VSI-Bench")
 ```
-
-Additionally, we provide the compressed raw videos in `*.zip`.
-
+Additionally, we provide the videos in `*.zip`.
 
 ## Dataset Description
-VSI-Bench quantitatively evaluate the visual-spatial intelligence of MLLMs from egocentric video. VSI-Bench comprises over 5,000 question-answer pairs derived from 288 real videos. These videos are sourced from the validation sets of the public indoor 3D scene reconstruction datasets `ScanNet`, `ScanNet++`, and `ARKitScenes` and represent diverse environments -- including residential spaces, professional settings (e.g., offices, labs), and industrial spaces (e.g., factories) and multiple geographic regions. Repurposing these existing 3D reconstruction and understanding datasets offers accurate object-level annotations which we use in question generation and could enable future study into the connection between MLLMs and 3D reconstruction.
+VSI-Bench quantitatively evaluates the visual-spatial intelligence of MLLMs from egocentric video. VSI-Bench comprises over 5,000 question-answer pairs derived from 288 real videos. These videos are sourced from the validation sets of the public indoor 3D scene reconstruction datasets `ScanNet`, `ScanNet++`, and `ARKitScenes`, and represent diverse environments -- including residential spaces, professional settings (e.g., offices, labs), and industrial spaces (e.g., factories) and multiple geographic regions. By repurposing these existing 3D reconstruction and understanding datasets, VSI-Bench benefits from accurate object-level annotations, which are used in question generation and could support future studies exploring the connection between MLLMs and 3D reconstruction.
 
 The dataset contains the following fields:
 
@@ -47,49 +44,17 @@ The dataset contains the following fields:
 | :--------- | :---------- |
 | `idx` | Global index of the entry in the dataset |
 | `dataset` | Video source: `scannet`, `arkitscenes` or `scannetpp` |
+| `scene_name` | Scene (video) name for each question-answer pair |
 | `question_type` | The type of task for question |
 | `question` | Question asked about the video |
-| `options` | Answer choices for the question (only for multiple choice questions) |
-| `ground_truth` | Correct answer to the question |
-| `video_suffix` | Suffix of the video  |
-
+| `options` | Choices for the question (only for multiple choice questions) |
+| `ground_truth` | Ground truth answer for the question |
 
-<br>
+## Evaluation
 
+VSI-Bench evaluates performance using two metrics: for multiple-choice questions, we use `Accuracy`, calculated based on exact matches. For numerical-answer questions, we introduce a new metric, `MRA (Mean Relative Accuracy)`, to assess how closely model predictions align with ground truth values.
 
-### Example Code
-
-<!-- @shusheng -->
-```python
-import pandas as pd
-# Load the CSV file into a DataFrame
-df = pd.read_csv('cv_bench_results.csv')
-# Define a function to calculate accuracy for a given source
-def calculate_accuracy(df, source):
-    source_df = df[df['source'] == source]
-    accuracy = source_df['result'].mean()  # Assuming 'result' is 1 for correct and 0 for incorrect
-    return accuracy
-# Calculate accuracy for each source
-accuracy_2d_ade = calculate_accuracy(df, 'ADE20K')
-accuracy_2d_coco = calculate_accuracy(df, 'COCO')
-accuracy_3d_omni = calculate_accuracy(df, 'Omni3D')
-# Calculate the accuracy for each type
-accuracy_2d = (accuracy_2d_ade + accuracy_2d_coco) / 2
-accuracy_3d = accuracy_3d_omni
-# Compute the combined accuracy as specified
-combined_accuracy = (accuracy_2d + accuracy_3d) / 2
-# Print the results
-print(f"CV-Bench Accuracy: {combined_accuracy:.4f}")
-print()
-print(f"Type Accuracies:")
-print(f"2D Accuracy: {accuracy_2d:.4f}")
-print(f"3D Accuracy: {accuracy_3d:.4f}")
-print()
-print(f"Source Accuracies:")
-print(f"ADE20K Accuracy: {accuracy_2d_ade:.4f}")
-print(f"COCO Accuracy: {accuracy_2d_coco:.4f}")
-print(f"Omni3D Accuracy: {accuracy_3d_omni:.4f}")
-```
+We provide an out-of-the-box evaluation of VSI-Bench in our [GitHub repository](https://github.com/vision-x-nyu/thinking-in-space), including the [metrics](https://github.com/vision-x-nyu/thinking-in-space/blob/main/lmms_eval/tasks/vsibench/utils.py#L109C1-L155C36) implementation used in our framework. For further detailes, users can refer to our paper and GitHub repository.
 
 ## Citation
 

arkitscenes.zip

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+size 1812227830

scannet.zip

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+size 2885230719

scannetpp.zip

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+size 1030992424

test-00000-of-00001.parquet

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