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--- |
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license: creativeml-openrail-m |
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task_categories: |
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- image-segmentation |
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- image-classification |
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- image-feature-extraction |
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language: |
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- en |
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pretty_name: SleetView Agentic Ai Dataset |
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size_categories: |
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- n<1K |
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--- |
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# The SleetView Agentic AI Dataset |
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The SleetView Agentic AI dataset is a collection of synthetic content automatically generated using Agentic AI |
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## Dataset Details |
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### Dataset Description |
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The images were generated with a collection of models available under the Apache-2.0 or creativeml-openrail-m licenses. |
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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. |
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As pioneers in the synthetic content detection realm, we think having a varied sampling of synthetic data is important to determine detection efficiency. |
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This dataset allows evaluation for the following scenarios: |
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* Varied aspect ratios including multiple resolutions common in digital systems |
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* Landscapes, portraits with one or more characters, pets and other animals, automobiles and architecture |
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* Multiple lighting scenarios including ambient light, spot lights, night time/day time |
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Also included in the dataset are segmentation masks and metadata generated with the DETR panoptic model: https://huggingface.co/facebook/detr-resnet-101-panoptic |
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- **Shared by:** Mendit.AI |
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- **License:** creativeml-openrail-m |
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## Uses |
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This dataset can be useful for the following research areas: |
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* Synthetic content detection |
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* Evaluation of the quality of Agentic AI generated synthetic datasets |
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* Image segementation quality based on different composition and lighting scenarios |
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### Out-of-Scope Use |
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Please refer to the creativeml-openrail-m license for restrictions |
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## Dataset Structure |
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The dataset contains 248 images with the following structure: |
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* Image |
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* Associated segmentation mask |
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* Segmentation metadata formatted as json |
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## Dataset Creation |
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### Agentic AI Generation |
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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 |
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We opted for a local setup using Ollama and Falcon3 as the LLM powering the agent. |
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Based on our experience with this process we find: |
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* Instruction tuned LLMs that do not including reasoning are the best for this task |
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### Annotations [optional] |
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Segmentation masks and metadata were automatically generated using the DETR panoptic model: https://huggingface.co/facebook/detr-resnet-101-panoptic |
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## Citation [optional] |
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**BibTeX:** |
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@article{DBLP:journals/corr/abs-2005-12872, |
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author = {Nicolas Carion and |
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Francisco Massa and |
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Gabriel Synnaeve and |
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Nicolas Usunier and |
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Alexander Kirillov and |
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Sergey Zagoruyko}, |
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title = {End-to-End Object Detection with Transformers}, |
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journal = {CoRR}, |
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volume = {abs/2005.12872}, |
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year = {2020}, |
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url = {https://arxiv.org/abs/2005.12872}, |
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archivePrefix = {arXiv}, |
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eprint = {2005.12872}, |
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timestamp = {Thu, 28 May 2020 17:38:09 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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@misc{Falcon3, |
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title = {The Falcon 3 Family of Open Models}, |
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url = {https://huggingface.co/blog/falcon3}, |
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author = {Falcon-LLM Team}, |
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month = {December}, |
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year = {2024} |
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} |
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