---
license: creativeml-openrail-m
task_categories:
- image-segmentation
- image-classification
- image-feature-extraction
language:
- en
pretty_name: SleetView Agentic Ai Dataset
size_categories:
- n<1K
---
# The SleetView Agentic AI Dataset

The SleetView Agentic AI dataset is a collection of synthetic content automatically generated using Agentic AI

## Dataset Details

### Dataset Description

The images were generated with a collection of models available under the Apache-2.0 or creativeml-openrail-m licenses.
To generate this dataset we used our own agentic implementation given the goal of creating a dataset that can be used to research synthetic content detection.
As pioneers in the synthetic content detection realm, we think having a varied sampling of synthetic data is important to determine detection efficiency. 

This dataset allows evaluation for the following scenarios:
* Varied aspect ratios including multiple resolutions common in digital systems
* Landscapes, portraits with one or more characters, pets and other animals, automobiles and architecture
* Multiple lighting scenarios including ambient light, spot lights, night time/day time

Also included in the dataset are segmentation masks and metadata generated with the DETR panoptic model: https://huggingface.co./facebook/detr-resnet-101-panoptic 

- **Shared by:** Mendit.AI
- **License:** creativeml-openrail-m

## Uses

This dataset can be useful for the following research areas:
* Synthetic content detection
* Evaluation of the quality of Agentic AI generated synthetic datasets
* Image segementation quality based on different composition and lighting scenarios 

### Out-of-Scope Use

Please refer to the creativeml-openrail-m license for restrictions

## Dataset Structure

The dataset contains 248 images with the following structure:
* Image
* Associated segmentation mask
* Segmentation metadata formatted as json

## Dataset Creation

### Agentic AI Generation

An in depth explanation of our approach to agentic generation of synthetic content can be found here: https://menditai.substack.com/p/the-night-the-dataset-appeared-an
We opted for a local setup using Ollama and Falcon3 as the LLM powering the agent.
Based on our experience with this process we find:
* Instruction tuned LLMs that do not including reasoning are the best for this task

### Annotations [optional]

Segmentation masks and metadata were automatically generated using the DETR panoptic model: https://huggingface.co./facebook/detr-resnet-101-panoptic

## Citation [optional]

**BibTeX:**

@article{DBLP:journals/corr/abs-2005-12872,
  author    = {Nicolas Carion and
               Francisco Massa and
               Gabriel Synnaeve and
               Nicolas Usunier and
               Alexander Kirillov and
               Sergey Zagoruyko},
  title     = {End-to-End Object Detection with Transformers},
  journal   = {CoRR},
  volume    = {abs/2005.12872},
  year      = {2020},
  url       = {https://arxiv.org/abs/2005.12872},
  archivePrefix = {arXiv},
  eprint    = {2005.12872},
  timestamp = {Thu, 28 May 2020 17:38:09 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{Falcon3,
    title = {The Falcon 3 Family of Open Models},
    url = {https://huggingface.co./blog/falcon3},
    author = {Falcon-LLM Team},
    month = {December},
    year = {2024}
}