README.md

---
annotations_creators: []
language: en
size_categories:
- 1K<n<10K
task_categories:
- image-classification
- object-detection
task_ids: []
pretty_name: mind2web_multimodal_test_domain
tags:
- fiftyone
- visual-agents
- os-agents
- gui-grounding
- image
- image-classification
- object-detection
dataset_summary: '




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 4050 samples.


  ## Installation


  If you haven''t already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include ''max_samples'', etc

  dataset = load_from_hub("Voxel51/mind2web_multimodal_test_domain")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

  '
---

# Dataset Card for "Cross-Domain" Test Split in Multimodal Mind2Web

**Note**: This dataset is the test split of the Cross-Domain dataset introduced in the paper.

![image/png](m2w_td.gif)


This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 4050 samples.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/mind2web_multimodal_test_domain")

# Launch the App
session = fo.launch_app(dataset)
```

## Dataset Description
**Curated by:** The Ohio State University NLP Group (OSU-NLP-Group)  
**Shared by:** OSU-NLP-Group on Hugging Face  
**Language(s) (NLP):** en  
**License:** OPEN-RAIL License

## Dataset Sources
**Repository:** https://github.com/OSU-NLP-Group/SeeAct and https://huggingface.co/datasets/osunlp/Multimodal-Mind2Web  
**Paper:** "GPT-4V(ision) is a Generalist Web Agent, if Grounded" by Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, Yu Su  
**Demo:** https://osu-nlp-group.github.io/SeeAct

## Uses
### Direct Use
- Evaluating web agents' ability to generalize to entirely new domains
- Testing zero-shot domain transfer capabilities of models
- Benchmarking the true generalist capabilities of web agents
- Assessing model performance in unseen web environments

### Out-of-Scope Use
- Developing web agents for harmful purposes (as stated in the paper's impact statement)
- Automating actions that could violate website terms of service
- Creating agents that access users' personal profiles or perform sensitive operations without consent

## Dataset Structure
- Contains 694 tasks across 13 domains and 53 websites
- Tasks average 5.9 actions each
- Average 4,314 visual tokens per task
- Average 494 HTML elements per task
- Average 91,163 HTML tokens per task
- Each example includes task descriptions, HTML structure, operations (CLICK, TYPE, SELECT), target elements with attributes, and action histories

### FiftyOne Dataset Structure

**Basic Info:** 1,338 web UI screenshots with task-based annotations

**Core Fields:**
- `action_uid`: StringField - Unique action identifier
- `annotation_id`: StringField - Annotation identifier
- `target_action_index`: IntField - Index of target action in sequence
- `ground_truth`: EmbeddedDocumentField(Detection) - Element to interact with:
  - `label`: Action type (TYPE, CLICK)
  - `bounding_box`: a list of relative bounding box coordinates in [0, 1] in the following format: `<top-left-x>, <top-left-y>, <width>, <height>]`
  - `target_action_reprs`: String representation of target action
- `website`: EmbeddedDocumentField(Classification) - Website name
- `domain`: EmbeddedDocumentField(Classification) - Website domain category
- `subdomain`: EmbeddedDocumentField(Classification) - Website subdomain category
- `task_description`: StringField - Natural language description of the task
- `full_sequence`: ListField(StringField) - Complete sequence of actions for the task
- `previous_actions`: ListField - Actions already performed in the sequence
- `current_action`: StringField - Action to be performed
- `alternative_candidates`: EmbeddedDocumentField(Detections) - Other possible elements


## Dataset Creation
### Curation Rationale
The Cross-Domain split was specifically designed to evaluate an agent's ability to generalize to entirely new domains it hasn't encountered during training, representing the most challenging generalization scenario.

### Source Data
#### Data Collection and Processing
- Based on the original MIND2WEB dataset
- Each HTML document is aligned with its corresponding webpage screenshot image
- Underwent human verification to confirm element visibility and correct rendering for action prediction
- Specifically includes websites from top-level domains held out from the training data

#### Who are the source data producers?
Web screenshots and HTML were collected from 53 websites across 13 domains that were not represented in the training data.

### Annotations
#### Annotation process
Each task includes annotated action sequences showing the correct steps to complete the task. These were likely captured through a tool that records user actions on websites.

#### Who are the annotators?
Researchers from The Ohio State University NLP Group or hired annotators, though specific details aren't provided in the paper.

### Personal and Sensitive Information
The dataset focuses on non-login tasks to comply with user agreements and avoid privacy issues.

## Bias, Risks, and Limitations
- This split presents the most challenging generalization scenario as it tests performance on entirely unfamiliar domains
- In-context learning methods with large models show better performance than supervised fine-tuning on this split
- The gap between SEEACTOracle and other methods is largest in this split (23.2% step success rate difference)
- Website layouts and functionality may change over time, affecting the validity of the dataset
- Limited to the specific domains included; may not fully represent all possible web domains

## Citation

### BibTeX:

```bibtex
@article{zheng2024seeact,
  title={GPT-4V(ision) is a Generalist Web Agent, if Grounded},
  author={Boyuan Zheng and Boyu Gou and Jihyung Kil and Huan Sun and Yu Su},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024},
  url={https://openreview.net/forum?id=piecKJ2DlB},
}

@inproceedings{deng2023mindweb,
  title={Mind2Web: Towards a Generalist Agent for the Web},
  author={Xiang Deng and Yu Gu and Boyuan Zheng and Shijie Chen and Samuel Stevens and Boshi Wang and Huan Sun and Yu Su},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023},
  url={https://openreview.net/forum?id=kiYqbO3wqw}
}
```
### APA:
Zheng, B., Gou, B., Kil, J., Sun, H., & Su, Y. (2024). GPT-4V(ision) is a Generalist Web Agent, if Grounded. arXiv preprint arXiv:2401.01614.

## Dataset Card Contact
GitHub: https://github.com/OSU-NLP-Group/SeeAct