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@@ -3,7 +3,7 @@ license: mit
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  tags:
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  - art
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  size_categories:
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- - n<1K
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  ---
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  ## Nebula Image Dataset
@@ -17,7 +17,7 @@ The dataset includes about one hundred high-resolution images of different nebul
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  ### Data Format
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  The dataset is stored in the Parquet file format, which is a columnar data format that provides efficient storage and query performance. The Parquet file contains a single column, `train`, which holds the file paths to the individual nebula images.
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- ### Usage
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  You can use this dataset for a variety of machine learning and computer vision tasks, such as:
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  - Nebula classification
@@ -25,14 +25,12 @@ You can use this dataset for a variety of machine learning and computer vision t
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  - Nebula generation or synthesis
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  - Astronomical image processing and analysis
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- To get started, you can load the Parquet file using a library like Pandas or PyArrow, and then access the image paths to load the individual images for your project.
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  ### License
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  This dataset is licensed under the [MIT License](LICENSE). You are free to use, modify, and distribute the data as long as you provide attribution.
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- ### Citation
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- If you use this dataset in your research or project, please cite the following:
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-
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  ```py
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  from datasets import load_dataset
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  tags:
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  - art
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  size_categories:
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+ - 1K<n<10K
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  ---
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  ## Nebula Image Dataset
 
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  ### Data Format
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  The dataset is stored in the Parquet file format, which is a columnar data format that provides efficient storage and query performance. The Parquet file contains a single column, `train`, which holds the file paths to the individual nebula images.
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+ ## Usage
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  You can use this dataset for a variety of machine learning and computer vision tasks, such as:
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  - Nebula classification
 
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  - Nebula generation or synthesis
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  - Astronomical image processing and analysis
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+ To get started, cite the datasets section.
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  ### License
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  This dataset is licensed under the [MIT License](LICENSE). You are free to use, modify, and distribute the data as long as you provide attribution.
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+ ### Datasets
 
 
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  ```py
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  from datasets import load_dataset
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