|
--- |
|
license: apache-2.0 |
|
task_categories: |
|
- multiple-choice |
|
- visual-question-answering |
|
language: |
|
- en |
|
size_categories: |
|
- n<1K |
|
configs: |
|
- config_name: benchmark |
|
data_files: |
|
- split: test |
|
path: dataset.json |
|
paperswithcode_id: mapeval-visual |
|
tags: |
|
- geospatial |
|
--- |
|
|
|
# MapEval-Visual |
|
|
|
This dataset was introduced in [MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models](https://arxiv.org/abs/2501.00316) |
|
|
|
# Example |
|
|
|
 |
|
|
|
#### Query |
|
I am presently visiting Mount Royal Park . Could you please inform me about the nearby historical landmark? |
|
|
|
#### Options |
|
1. Circle Stone |
|
2. Secret pool |
|
3. Maison William Caldwell Cottingham |
|
4. Poste de cavalerie du Service de police de la Ville de Montreal |
|
|
|
#### Correct Option |
|
1. Circle Stone |
|
|
|
# Prerequisite |
|
|
|
Download the [Vdata.zip](https://huggingface.co/datasets/MapEval/MapEval-Visual/resolve/main/Vdata.zip?download=true) and extract in the working directory. This directory contains all the images. |
|
|
|
# Usage |
|
```python |
|
from datasets import load_dataset |
|
import PIL.Image |
|
# Load dataset |
|
ds = load_dataset("MapEval/MapEval-Visual", name="benchmark") |
|
|
|
for item in ds["test"]: |
|
|
|
# Start with a clear task description |
|
prompt = ( |
|
"You are a highly intelligent assistant. " |
|
"Based on the given image, answer the multiple-choice question by selecting the correct option.\n\n" |
|
"Question:\n" + item["question"] + "\n\n" |
|
"Options:\n" |
|
) |
|
|
|
# List the options more clearly |
|
for i, option in enumerate(item["options"], start=1): |
|
prompt += f"{i}. {option}\n" |
|
|
|
# Add a concluding sentence to encourage selection of the answer |
|
prompt += "\nSelect the best option by choosing its number." |
|
|
|
# Load image from Vdata/ directory |
|
img = PIL.Image.open(item["context"]) |
|
|
|
# Use the prompt as needed |
|
print([prompt, img]) # Replace with your processing logic |
|
|
|
# Then match the output with item["answer"] or item["options"][item["answer"]-1] |
|
# If item["answer"] == 0: then it's unanswerable |
|
``` |
|
|
|
# Leaderboard |
|
|
|
| Model | Overall | Place Info | Nearby | Routing | Counting | Unanswerable | |
|
|---------------------------|:-------:|:----------:|:------:|:-------:|:--------:|:------------:| |
|
| Claude-3.5-Sonnet | **61.65** | **82.64** | 55.56 | **45.00** | **47.73** | **90.00** | |
|
| GPT-4o | 58.90 | 76.86 | **57.78** | 50.00 | **47.73** | 40.00 | |
|
| Gemini-1.5-Pro | 56.14 | 76.86 | 56.67 | 43.75 | 32.95 | 80.00 | |
|
| GPT-4-Turbo | 55.89 | 75.21 | 56.67 | 42.50 | 44.32 | 40.00 | |
|
| Gemini-1.5-Flash | 51.94 | 70.25 | 56.47 | 38.36 | 32.95 | 55.00 | |
|
| GPT-4o-mini | 50.13 | 77.69 | 47.78 | 41.25 | 28.41 | 25.00 | |
|
| Qwen2-VL-7B-Instruct | 51.63 | 71.07 | 48.89 | 40.00 | 40.91 | 40.00 | |
|
| Glm-4v-9b | 48.12 | 73.55 | 42.22 | 41.25 | 34.09 | 10.00 | |
|
| InternLm-Xcomposer2 | 43.11 | 70.41 | 48.89 | 43.75 | 34.09 | 10.00 | |
|
| MiniCPM-Llama3-V-2.5 | 40.60 | 60.33 | 32.22 | 32.50 | 31.82 | 30.00 | |
|
| Llama-3-VILA1.5-8B | 32.99 | 46.90 | 32.22 | 28.75 | 26.14 | 5.00 | |
|
| DocOwl1.5 | 31.08 | 43.80 | 23.33 | 32.50 | 27.27 | 0.00 | |
|
| Llava-v1.6-Mistral-7B-hf | 31.33 | 42.15 | 28.89 | 32.50 | 21.59 | 15.00 | |
|
| Paligemma-3B-mix-224 | 30.58 | 37.19 | 25.56 | 38.75 | 23.86 | 10.00 | |
|
| Llava-1.5-7B-hf | 20.05 | 22.31 | 18.89 | 13.75 | 28.41 | 0.00 | |
|
| Human | 82.23 | 81.67 | 82.42 | 85.18 | 78.41 | 65.00 | |
|
|
|
# Citation |
|
|
|
If you use this dataset, please cite the original paper: |
|
|
|
``` |
|
@article{dihan2024mapeval, |
|
title={MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models}, |
|
author={Dihan, Mahir Labib and Hassan, Md Tanvir and Parvez, Md Tanvir and Hasan, Md Hasebul and Alam, Md Almash and Cheema, Muhammad Aamir and Ali, Mohammed Eunus and Parvez, Md Rizwan}, |
|
journal={arXiv preprint arXiv:2501.00316}, |
|
year={2024} |
|
} |
|
``` |
