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  # Dataset Card for AFO - Aerial Floating Objects
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- <!-- Provide a quick summary of the dataset. -->
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  ### Dataset Description
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- <!-- Provide a longer summary of what this dataset is. -->
 
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- - **Curated by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Language(s) (NLP):** en
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- - **License:** [More Information Needed]
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- ### Dataset Sources [optional]
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- <!-- Provide the basic links for the dataset. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the dataset is intended to be used. -->
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- ### Direct Use
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- <!-- This section describes suitable use cases for the dataset. -->
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- [More Information Needed]
 
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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- [More Information Needed]
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  ## Dataset Structure
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- <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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- ## Dataset Creation
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- ### Curation Rationale
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- <!-- Motivation for the creation of this dataset. -->
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- ### Source Data
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- <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
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- #### Data Collection and Processing
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- <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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- #### Who are the source data producers?
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- <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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- [More Information Needed]
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- ### Annotations [optional]
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- <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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- #### Annotation process
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- <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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- #### Who are the annotators?
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- <!-- This section describes the people or systems who created the annotations. -->
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- #### Personal and Sensitive Information
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- <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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  **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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- ## More Information [optional]
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- ## Dataset Card Authors [optional]
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- ## Dataset Card Contact
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- [More Information Needed]
 
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  # Dataset Card for AFO - Aerial Floating Objects
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+ AFO dataset is the first free dataset for training machine learning and deep learning models for maritime Search and Rescue applications. It contains aerial-drone videos with 40,000 hand-annotated persons and objects floating in the water, many of small size, which makes them difficult to detect.
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  ### Dataset Description
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+ The AFO dataset contains images taken from fifty video clips containing objects floating on the water surface, captured by the various drone-mounted cameras (from 1280x720 to 3840x2160 resolutions), which have been used to create AFO. From these videos, we have extracted and manually annotated 3647 images that contain 39991 objects. These have been then split into three parts: the training (67,4% of objects), the test (19,12% of objects), and the validation set (13,48% of objects). In order to prevent overfitting of the model to the given data, the test set contains selected frames from nine videos that were not used in either the training or validation sets.
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+ Dataset is prepared in Darknet YOLO format -> https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects
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+ - **Funded by [optional]:** Polish National Science Center
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+ - **Language(s) (NLP):** en
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+ - **License:** Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License
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+ ### Dataset Sources
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+ - **Repository:** https://www.kaggle.com/datasets/jangsienicajzkowy/afo-aerial-dataset-of-floating-objects
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+ - **Paper:** An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance
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+ ## Uses
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+ The datasets provided on this page are published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. If you are interested in commercial usage you can contact authors for further options.
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  ## Dataset Structure
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+ Ground_truth field plus clip embeddings are included!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Citation
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  **BibTeX:**
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+ @article{article,
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+ author = {Gąsienica-Józkowy, Jan and Knapik, Mateusz and Cyganek, Boguslaw},
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+ year = {2021},
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+ month = {01},
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+ pages = {1-15},
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+ title = {An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance},
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+ journal = {Integrated Computer-Aided Engineering},
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+ doi = {10.3233/ICA-210649}
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+ }
 
 
 
 
 
 
 
 
 
 
 
 
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