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@@ -71,6 +71,8 @@ The cut outs are 512x512 pixel jpg images centred on the galaxy source.
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  I've split away 1% of the images into a test set, and 1% into a validation set.
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  The remaining 98% of the images comprise the training set.
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  There is also accompanying metadata!
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  To combine the metadata with the galaxy images you can do (for example):
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  ```python
@@ -88,6 +90,17 @@ galaxies = load_dataset("Smith42/galaxies", revision="v2.0", streaming=True)
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  The metadata is also available in parquet format in the root dir of this repo.
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  You can link the metadata with the galaxies via their dr8_id.
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  ## Useful links
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  Paper here: [https://arxiv.org/abs/2405.14930](https://arxiv.org/abs/2405.14930)
 
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  I've split away 1% of the images into a test set, and 1% into a validation set.
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  The remaining 98% of the images comprise the training set.
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+ ## Metadata (galaxy properties)
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+
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  There is also accompanying metadata!
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  To combine the metadata with the galaxy images you can do (for example):
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  ```python
 
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  The metadata is also available in parquet format in the root dir of this repo.
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  You can link the metadata with the galaxies via their dr8_id.
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+ ## Embeddings from the pre-trained family of AstroPTv2 models
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+
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+ Same story with pre-generated embeddings from [AstroPTv2](https://huggingface.co/smith42/astropt_v2.0).
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+
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+ You can combine the embeddings with galaxy imagery and metadata by doing:
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+ ```python
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+ galaxies = load_dataset("Smith42/galaxies", revision="v2.0", streaming=True)
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+ embs = load_dataset("Smith42/galaxies_embeddings", streaming=True)
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+ combined = concatenate_datasets([galaxies['train'], embs['train']], axis=1)
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+ ```
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+
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  ## Useful links
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  Paper here: [https://arxiv.org/abs/2405.14930](https://arxiv.org/abs/2405.14930)