| --- |
| 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} |
| } |
| ``` |
