---
license: cc-by-nc-sa-4.0
task_categories:
- video-classification
- visual-question-answering
- question-answering
language:
- en
size_categories:
- n<1K
---

# MORSE-500 Benchmark

<table>
  <tr>
    <td style="padding: 0;">
      <a href="https://morse-500.github.io">
        <img src="https://img.shields.io/badge/Website-1E90FF?style=for-the-badge&logo=firefox&logoColor=ffffff&labelColor" alt="Website">
      </a>
    </td>
    <td style="padding: 0;">
      <a href="https://huggingface.co/datasets/video-reasoning/morse-500">
        <img src="https://img.shields.io/badge/Data-D5A848?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor" alt="Data">
      </a>
    </td>
    <td style="padding: 0;">
      <a href="https://huggingface.co/datasets/video-reasoning/morse-500-view">
        <img src="https://img.shields.io/badge/View-D5A848?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor" alt="Viewer">
      </a>
    </td>
    <td style="padding: 0;">
      <a href="https://github.com/morse-benchmark/morse-500">
        <img src="https://img.shields.io/badge/Code-000000?style=for-the-badge&logo=github&logoColor=white" alt="Code">
      </a>
    </td>
    <td style="padding: 0;">
      <a href="https://arxiv.org/abs/2506.05523">
        <img src="https://img.shields.io/badge/arXiv-2506.05523-b31b1b.svg?style=for-the-badge" alt="arXiv">
      </a>
    </td>
  </tr>
</table>

## 🔥 News
- **May 15, 2025**: We release **`MORSE-500`**, 500 programmatically generated videos across six reasoning categories: abstract, mathematical, physical, planning, spatial, and temporal, to stress-test multimodal reasoning. Frontier models including OpenAI o3 and Gemini 2.5 Pro score lower than 25% accuracy (see 🏆[`Leaderboard`](https://morse-500.github.io/#leaderboard)).
- **Visit 🤗 Data: [`morse-500`](https://huggingface.co/datasets/video-reasoning/morse-500) for newer updates**

## 📦 Resource
- 🌐 Websie: [`morse-500`](https://morse-500.github.io)
- 🤗 Data: [`morse-500`](https://huggingface.co/datasets/video-reasoning/morse-500)
- 🤗 Video Viewer: [`morse-500-view`](https://huggingface.co/datasets/video-reasoning/morse-500-view)
- 💻 Code: [`morse-500`](https://github.com/morse-benchmark/morse-500)
- 📝 Paper: [`arXiv:2506.05523`](https://arxiv.org/abs/2506.05523)


## ✨ Key Features

| Aspect | Details |
| --- | --- |
| **Fresh & Portable** | 500 newly cooked video clips + CSV metadata that runs fast |
| **Scalable Difficulty** | Videos are generated programmatically so we can dial up complexity and release harder versions as models improve |
| **Diverse Categories** | Spanning  *Abstract, Mathematical, Physical, Planning, Spatial, Temporal (+ Causal)* – a vibrant mix of the reasoning types that matter |
| **Pure Visual Reasoning** | Questions are baked right into the videos. No text crutches, no shortcuts – if you can't see it, you can't solve it |
| **Developer-Friendly** | A “[-view](https://huggingface.co/datasets/video-reasoning/morse-500-view)” subset streams directly on **Hugging Face**, making browsing and debugging smoother than a sunny afternoon |


## 📂 Dataset Structure

- `test.csv`: Contains the dataset metadata, including video file name, query, ground_truth, question_text, and category
- `test.zip`: Contains all MP4 video files
- `test_sz512.zip`: Contains MP4 video files resized to 512px for longside while keeping the original aspect ratio

## ⚡ Quick Start

```bash
### In bash ###
# download the videos
git clone https://huggingface.co/datasets/video-reasoning/morse-500

# unzip the videos
cd morse-500
# unzip test.zip -d test                # original size
unzip test_sz512.zip -d test_sz512    # long side resized to 512


### In python ###
# load dataset metadata ("idx", "video", "query", "question_text", "ground_truth", "category")
from datasets import load_dataset
dataset = load_dataset('video-reasoning/morse-500')
dataset = dataset['test']
video_root = 'test_sz512' # use the resize videos

# run your model on the benchmark
for i, example in enumerate(dataset):
  video_path = f"{video_root}/" + example["video"]
  print(f"Processing {i} {video_path}")
  query = "Answer the question in this video."
  gt = example['ground_truth']

  # if your model has video support
  answer = query_video(model_name, video_path, query)
  # otherwise query with image frames, default 2 fps capped at 32 total frames
  # answer = query_video_frames(model_name, video_path, query, fps=2, max_num_frames=32)
  
  print(f"Answer: {answer}")
  print(f"GT: {gt}")
```

Example query_video function
```python
model_name = "xxx"
openai_api_key = "xxx"
openai_api_base = "xxx"
client = OpenAI(
  api_key=openai_api_key,
  base_url=openai_api_base,
)


def encode_b64(file_path):
  with open(file_path, "rb") as file:
      return base64.b64encode(file.read()).decode("utf-8")

base64_video = encode_b64(video_path)
video_url = f"data:video/mp4;base64,{base64_video}"

response = client.chat.completions.create(
    model=model_name,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": query
                },
                {
                    "type": "video_url",
                    "video_url": {"url": video_url},
                },
            ],
        }
    ],
)

result = response.choices[0].message.content
print(result)
```
## More scripts can be found on Github https://github.com/morse-benchmark/morse-500