Added dataset card

README.md

@@ -1,3 +1,95 @@
----
-license: creativeml-openrail-m
----
+---
+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}
+}