Update README.md

README.md

@@ -77,8 +77,10 @@ size_categories:
 # Dataset Card for ESA Hubble Deep Space Images & Captions
 
 ## Table of Contents
+
 - [Dataset Description](#dataset-description)
   - [Dataset Summary](#dataset-summary)
+  - [Examples](#examples)
   - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
   - [Languages](#languages)
 - [Dataset Structure](#dataset-structure)
@@ -103,65 +105,67 @@ size_categories:
 ## Dataset Description
 
 - **Homepage:** [ESA Hubble](https://esahubble.org/)
-- **Repository:** [Hubble Diffuser repository](https://github.com/Supermaxman/HubbleDiffuser)
+- **Repository:** [Hubble Diffusion repository](https://github.com/Supermaxman/hubble-diffusion)
 - **Point of Contact:** [Maxwell Weinzierl](mailto:maxwell.weinzierl@utdallas.edu)
 
 ### Dataset Summary
-The ESA Hubble Deep Space Images & Captions dataset is composed primarily of Hubble deep space scans as high-resolution images, 
+
+The ESA Hubble Deep Space Images & Captions dataset is composed primarily of Hubble deep space scans as high-resolution images,
 along with textual descriptions written by ESA/Hubble. Metadata is also included, which enables more detailed filtering and understanding of massive space scans.
 The purpose of this dataset is to enable text-to-image generation methods for generating high-quality deep space scans from prompts.
+Check out [Hubble Diffusion v2](https://huggingface.co/Supermaxman/hubble-diffusion-2) for an example of a model trained on this dataset!
 
-#### Examples
-
-![opo9941a](https://cdn.spacetelescope.org/archives/images/thumb700x/opo9941a.jpg)
-> A grazing encounter between two spiral galaxies: In the direction of the constellation Canis Major, two spiral galaxies pass by each other like majestic ships in the night. The near-collision has been caught in images taken by the NASA/ESA Hubble Space Telescope and its Wide Field Planetary Camera 2.
+### Examples
 
+#### A grazing encounter between two spiral galaxies
 
+> In the direction of the constellation Canis Major, two spiral galaxies pass by each other like majestic ships in the night. The near-collision has been caught in images taken by the NASA/ESA Hubble Space Telescope and its Wide Field Planetary Camera 2.
+>![opo9941a](https://cdn.spacetelescope.org/archives/images/thumb700x/opo9941a.jpg)
+>
 > Credit: NASA/ESA and The Hubble Heritage Team (STScI)
 
+#### The magnificent starburst galaxy Messier 82
 
-![heic0604a](https://cdn.spacetelescope.org/archives/images/screen/heic0604a.jpg)
-> The magnificent starburst galaxy Messier 82: This mosaic image of the magnificent starburst galaxy, Messier 82 (M82) is the sharpest wide-angle view ever obtained of M82. It is a galaxy remarkable for its webs of shredded clouds and flame-like plumes of glowing hydrogen blasting out from its central regions where young stars are being born 10 times faster than they are inside in our Milky Way Galaxy.
-
-
+> This mosaic image of the magnificent starburst galaxy, Messier 82 (M82) is the sharpest wide-angle view ever obtained of M82. It is a galaxy remarkable for its webs of shredded clouds and flame-like plumes of glowing hydrogen blasting out from its central regions where young stars are being born 10 times faster than they are inside in our Milky Way Galaxy.
+>![heic0604a](https://cdn.spacetelescope.org/archives/images/screen/heic0604a.jpg)
+>
 > Credit: NASA, ESA and the Hubble Heritage Team (STScI/AURA). Acknowledgment: J. Gallagher (University of Wisconsin), M. Mountain (STScI) and P. Puxley (NSF).
 
+#### Extreme star cluster bursts into life in new Hubble image
 
-![heic0715a](https://cdn.spacetelescope.org/archives/images/screen/heic0715a.jpg)
-> Extreme star cluster bursts into life in new Hubble image: The star-forming region NGC 3603 - seen here in the latest Hubble Space Telescope image - contains one of the most impressive massive young star clusters in the Milky Way. Bathed in gas and dust the cluster formed in a huge rush of star formation thought to have occurred around a million years ago. The hot blue stars at the core are responsible for carving out a huge cavity in the gas seen to the right of the star cluster in NGC 3603's centre.
-
-
+> The star-forming region NGC 3603 - seen here in the latest Hubble Space Telescope image - contains one of the most impressive massive young star clusters in the Milky Way. Bathed in gas and dust the cluster formed in a huge rush of star formation thought to have occurred around a million years ago. The hot blue stars at the core are responsible for carving out a huge cavity in the gas seen to the right of the star cluster in NGC 3603's centre.
+>![heic0715a](https://cdn.spacetelescope.org/archives/images/screen/heic0715a.jpg)
+>
 > Credit: NASA, ESA and the Hubble Heritage (STScI/AURA)-ESA/Hubble Collaboration
 
-
 #### Statistics
 
- * There are a total of 2,706 deep space images
- * The complete uncompressed size of the dataset is 120 GB, so definitely make use of [Streaming](https://huggingface.co/docs/datasets/stream)
- * The average image is 44 MB, while the max image size is 432 MB
- * The average image has a height of 2,881 pixels, and an average width of 3,267 pixels
+- There are a total of 2,706 deep space images
+- The complete uncompressed size of the dataset is 120 GB, so definitely make use of [Streaming](https://huggingface.co/docs/datasets/stream)
+- The average image is 44 MB, while the max image size is 432 MB
+- The average image has a height of 2,881 pixels, and an average width of 3,267 pixels
 
 ### Supported Tasks and Leaderboards
- - `text-to-image`: The dataset can be used to train a model for conditional image generation from text. A conditional text-to-image generation model is presented with a text prompt, and is asked to generate an image which aligns with that text prompt. Model performance is typically measured by human judgement, as it is difficult to automatically measure the quality of generated images and how closely they match the text prompt. An example of a text-to-image model is [Stable Diffusion v2-1](stabilityai/stable-diffusion-2-1). 
 
-### Languages
+- `text-to-image`: The dataset can be used to train a model for conditional image generation from text. A conditional text-to-image generation model is presented with a text prompt, and is asked to generate an image which aligns with that text prompt. Model performance is typically measured by human judgement, as it is difficult to automatically measure the quality of generated images and how closely they match the text prompt. An example of a text-to-image model is [Stable Diffusion v2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1). An example of a text-to-image model trained on this dataset is [Hubble Diffusion v2](https://huggingface.co/Supermaxman/hubble-diffusion-2).
 
-The text in the dataset is in English, as written by the writers from ESA/Hubble at [https://esahubble.org/](https://esahubble.org/). The associated BCP-47 code is `en`.
+### Languages
 
+The text describing the images in the dataset is in English, as written by the writers from ESA/Hubble at [https://esahubble.org/](https://esahubble.org/). The associated BCP-47 code is `en`.
 
 ## Dataset Structure
 
 ### Data Instances
 
-A typical data point comprises a high-quality deep space scan as an image, along with a textual description of that image produced by ESA/Hubble. 
+A typical data point comprises a high-quality deep space scan as an image, along with a textual description of that image produced by ESA/Hubble.
 The textual description was derived by combining the `title` and the `description` of the image from the ESA/Hubble website.
 Additionally, each data point also contains significant metadata about the image, such as the type of image, credits, the URL, the release date, and more.
 
-
 An example looks as follows:
-```
+
+```json
 {
-   'image': <encoded image>,
+   "image": "<encoded image>",
    "text":"A grazing encounter between two spiral galaxies: In the direction of the constellation Canis Major, two spiral galaxies pass by each other like majestic ships in the night. The near-collision has been caught in images taken by the NASA/ESA Hubble Space Telescope and its Wide Field Planetary Camera 2.",
    "id":"opo9941a",
    "title":"A grazing encounter between two spiral galaxies",
@@ -217,57 +221,54 @@ An example looks as follows:
 - `crop_h`: height starting point of image if cropped, otherwise 0
 - `cropped`: whether this image needed to be cropped or not
 
-
 ### Data Splits
 
-The data is only provided in a single training split.
-
+The data is only provided in a single training split, as the purpose of the dataset is additional fine-tuning for the task of `text-to-image` generation.
 
 ## Dataset Creation
 
 ### Curation Rationale
-The ESA Hubble Deep Space Images & Captions dataset was built to provide ease of access to extremely high-quality Hubble deep space scans. 
+
+The ESA Hubble Deep Space Images & Captions dataset was built to provide ease of access to extremely high-quality Hubble deep space scans.
 Images from the Hubble telescope have already inspired millions, and the hope is that this dataset can be used to create inspiring models and approaches to further push interest in space & cosmology.
 
 ### Source Data
 
 #### Initial Data Collection
-All images were collected from [https://esahubble.org/](https://esahubble.org/). 
-Fullsize Original images & metadata were crawled from the ESA Hubble website using [Scrapy](https://scrapy.org/).
-Images were downloaded as `.tiff` files with the following [code](https://github.com/Supermaxman/SpaceGAN), while 
-additional metadata was later collected for each image using the following [code](https://github.com/Supermaxman/HubbleDiffuser).
 
+All images were collected from [https://esahubble.org/](https://esahubble.org/).
+Fullsize Original images & metadata were crawled from the ESA Hubble website using [Scrapy](https://scrapy.org/).
+Images were downloaded as `.tiff` files with the following [code](https://github.com/Supermaxman/SpaceGAN), while
+additional metadata was later collected for each image using the following [code](https://github.com/Supermaxman/hubble-diffusion).
 
 As the ESA Hubble website collects images from a wide variety of sources, images were filtered to try to avoid any non-space scan images as follows:
 The ESA Hubble [Advanced Image Search](http://esahubble.org/images/archive/search) enables the following filtering parameters:
- * images with Minimum size greater than or equal to 400x300
- * Ranking greater than or equal to Fair or better
- * Type containing 'Observation'
 
+- images with Minimum size greater than or equal to 400x300
+- Ranking greater than or equal to Fair or better
+- Type containing 'Observation'
 
 This reduced significantly the number of images which had nothing to do with Hubble deep space scans.
 A total of around 3,000 space images were collected with this method.
 
-
 #### Filtering
 
 Further automatic and manual filtering was performed to remove the following:
- * improperly classified images
- * space renders
- * diagrams with text
- * images of celestial bodies within our solar system
 
+- improperly classified images
+- space renders
+- diagrams with text
+- images of celestial bodies within our solar system
+- images with too low a resolution
 
 This brought the total number of deep space images down to 2,593.
 This process was not perfect, and there likely remain some images in the dataset that should be removed in the future.
 
-
-
 #### Preprocessing
 
-Some of the deep space scans were as large as 34,372x19,345, with a bit depth of 24 (nearly 2 GB). 
-Unfortunately, these images were too large to upload easily. 
-Therefore, images were automatically subdivided in half if they were above 12,000 pixels in either height or width. 
+Some of the deep space scans were as large as 34,372x19,345, with a bit depth of 24 (nearly 2 GB).
+Unfortunately, these images were too large to upload easily
+Therefore, images were automatically subdivided in half if they were above 12,000 pixels in either height or width.
 Subdivided images were also tagged with additional metadata, such that users can reconstruct the original images if they would prefer.
 Otherwise, metadata was copied across subdivided images.
 Additionally, images were converted from RGB/RGBX `.tiff` to RGB `.png` files to avoid encoding issues.
@@ -299,13 +300,11 @@ A system that succeeds at text-to-image generation would be able to generate ins
 
 ### Discussion of Biases
 
-It is unfortunate that we currently only have English captions for these deep space scans. 
+It is unfortunate that we currently only have English captions for these deep space scans.
 In the future, expanding these captions to more languages could help spread interest in astronomy far and wide.
 
-
 Additionally, these captions may be too technical for the average person to effectively utilize for a text-to-image model.
 
-
 ### Other Known Limitations
 
 [N/A]
@@ -317,19 +316,17 @@ Additionally, these captions may be too technical for the average person to effe
 The dataset was initially created by all the wonderful researchers, engineers, scientists, and more behind the Hubble Telescope, NASA, and the ESA.
 Maxwell Weinzierl collected, filtered, and preprocessed this data for ease of use.
 
-
 ### Licensing Information
 
-ESA/Hubble images, videos and web texts are released under the [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/) 
+ESA/Hubble images, videos and web texts are released under the [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/)
 and may on a non-exclusive basis be reproduced without fee provided they are clearly and visibly credited.
 See [https://esahubble.org/copyright/](https://esahubble.org/copyright/) for additional conditions for reproduction and copyright.
 
-
 ### Citation Information
 
 If you use this dataset, please cite it as:
 
-```
+```bibtex
 @misc{weinzierl2023hubble,
       author = {Weinzierl, Maxwell A.},
       title = {ESA Hubble Deep Space Images & Captions},
@@ -340,4 +337,4 @@ If you use this dataset, please cite it as:
 
 ### Contributions
 
-Thanks to [@supermaxman](https://github.com/supermaxman) for adding this dataset.
+Thanks to [@supermaxman](https://github.com/supermaxman) for adding this dataset.