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Correct task category, add paper link (#3)

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- Correct task category, add paper link (23b1d73c77bd0d25eda1c8d83728e89bb7f26a32)


Co-authored-by: Niels Rogge <[email protected]>

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  1. README.md +5 -12
README.md CHANGED
@@ -1,25 +1,18 @@
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  ---
 
 
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  license: apache-2.0
 
 
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  task_categories:
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- - image-segmentation
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  - image-classification
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- - image-to-text
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- task_ids:
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- - semantic-segmentation
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- - visual-question-answering
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- - image-captioning
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- language:
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- - en
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  tags:
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  - computer-vision
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  - photography
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- - segmentation
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  - annotations
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  - EXIF
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  - scene-understanding
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  - multimodal
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- size_categories:
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- - 1K<n<10K
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  dataset_info:
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  features:
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  - name: image_id
@@ -93,7 +86,7 @@ The DataSeeds.AI Sample Dataset (DSD) is a high-fidelity, human-curated computer
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  Each image includes multi-tier human annotations and semantic segmentation masks. Generously contributed to the community by the GuruShots photography platform, where users engage in themed competitions, the DSD uniquely captures aesthetic preference signals and high-quality technical metadata (EXIF) across an expansive diversity of photographic styles, camera types, and subject matter. The dataset is optimized for fine-tuning and evaluating multimodal vision-language models, especially in scene description and stylistic comprehension tasks.
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- * **Technical Report** - Discover the methodology and technical details behind the DSD.
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  * **Github Repo** - Access the complete weights and code which were used to evaluate the DSD -- [https://github.com/DataSeeds-ai/DSD-finetune-blip-llava](https://github.com/DataSeeds-ai/DSD-finetune-blip-llava)
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  This dataset is ready for commercial/non-commercial use.
 
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  ---
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+ language:
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+ - en
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  license: apache-2.0
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+ size_categories:
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+ - 1K<n<10K
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  task_categories:
 
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  - image-classification
 
 
 
 
 
 
 
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  tags:
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  - computer-vision
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  - photography
 
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  - annotations
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  - EXIF
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  - scene-understanding
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  - multimodal
 
 
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  dataset_info:
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  features:
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  - name: image_id
 
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  Each image includes multi-tier human annotations and semantic segmentation masks. Generously contributed to the community by the GuruShots photography platform, where users engage in themed competitions, the DSD uniquely captures aesthetic preference signals and high-quality technical metadata (EXIF) across an expansive diversity of photographic styles, camera types, and subject matter. The dataset is optimized for fine-tuning and evaluating multimodal vision-language models, especially in scene description and stylistic comprehension tasks.
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+ * **Technical Report** - [Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery](https://huggingface.co/papers/2506.05673)
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  * **Github Repo** - Access the complete weights and code which were used to evaluate the DSD -- [https://github.com/DataSeeds-ai/DSD-finetune-blip-llava](https://github.com/DataSeeds-ai/DSD-finetune-blip-llava)
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  This dataset is ready for commercial/non-commercial use.