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# Moon Detection Dataset for YOLOv8
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This dataset was developed as part of the CubeRT-02 project, a CubeSat mission aimed at testing AI-powered vision systems in aerospace contexts. It consists of 7500+ annotated images for object detection of the Moon, optimized for use with the YOLOv8 architecture.
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## 📸 Dataset Collection & Annotation
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**Initial set**: ~400 web-sourced images used for theoretical model exploration.
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**Main dataset**: 7500+ images captured over 6 months using smartphones and amateur camera devices, reflecting real-world scales, perspectives, and conditions.
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**Cleaning**: Manual filtering of blurry, low-quality, or irrelevant images.
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**Annotation**: Performed via Roboflow platform with bounding boxes for YOLOv8.
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**Augmentation**: Basic augmentations applied during preparation; none used during final training due to negative effects on performance.
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## 📁 Dataset Structure
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This dataset follows the YOLOv8 format. A Python script and a YAML configuration file are included to help you easily train or test the dataset using Ultralytics' YOLOv8 implementation.
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## Authors
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Luis Adrian Cabrera | https://github.com/LuisAdrian5519
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Jesse Banda Chaidez | https://github.com/Jessebnda
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## Contributors
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José Alejandro Padilla Pérez | https://github.com/PadillaPepe777
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Erick Blanco Nakashima
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Juan Pablo Riojas Pesqueira
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# Moon Detection Dataset for YOLOv8
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This dataset was developed as part of the CubeRT-02 project, a CubeSat mission aimed at testing AI-powered vision systems in aerospace contexts. It consists of 7500+ annotated images for object detection of the Moon, optimized for use with the YOLOv8 architecture.
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---
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## 📸 Dataset Collection & Annotation
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**Initial set**: ~400 web-sourced images used for theoretical model exploration.
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**Main dataset**: 7500+ images captured over 6 months using smartphones and amateur camera devices, reflecting real-world scales, perspectives, and conditions.
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| 9 |
**Cleaning**: Manual filtering of blurry, low-quality, or irrelevant images.
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| 10 |
**Annotation**: Performed via Roboflow platform with bounding boxes for YOLOv8.
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**Augmentation**: Basic augmentations applied during preparation; none used during final training due to negative effects on performance.
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---
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## 📁 Dataset Structure
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This dataset follows the YOLOv8 format. A Python script and a YAML configuration file are included to help you easily train or test the dataset using Ultralytics' YOLOv8 implementation.
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## Authors
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Luis Adrian Cabrera | https://github.com/LuisAdrian5519
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Jesse Banda Chaidez | https://github.com/Jessebnda
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## Contributors
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José Alejandro Padilla Pérez | https://github.com/PadillaPepe777
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Erick Blanco Nakashima
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Juan Pablo Riojas Pesqueira
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