Update README.md

README.md

@@ -83,18 +83,18 @@ configs:
     path: data/validation-*
 ---
 
-# GuruShots Sample Dataset (GSD)
+# DataSeeds Sample Dataset (DSD)
 
-![GSD Example](./GSD-example.jpeg)
+![DSD Example](./GSD-example.jpeg)
 
 ## Dataset Summary
 
-The GuruShots Sample Dataset (GSD) is a high-fidelity, human-curated computer vision-ready dataset comprised of 7,840 peer-ranked, fully annotated photographic images, 350,000+ words of descriptive text, and comprehensive metadata. While the GSD is being released under an open source license, a sister dataset of over 10,000 fully annotated and segmented images is available for immediate commercial licensing, and the broader GuruShots ecosystem contains over 100 million images in its catalog.
+The DataSeeds Sample Dataset (DSD) is a high-fidelity, human-curated computer vision-ready dataset comprised of 7,840 peer-ranked, fully annotated photographic images, 350,000+ words of descriptive text, and comprehensive metadata. While the DSD is being released under an open source license, a sister dataset of over 10,000 fully annotated and segmented images is available for immediate commercial licensing, and the broader GuruShots ecosystem contains over 100 million images in its catalog.
 
-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 GSD 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.
+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.
 
-* **Technical Report** - Discover the methodology and technical details behind the GSD.
-* **Github Repo** - Access the complete weights and code which were used to evaluate the GSD.
+* **Technical Report** - Discover the methodology and technical details behind the DSD.
+* **Github Repo** - Access the complete weights and code which were used to evaluate the DSD.
 
 This dataset is ready for commercial/non-commercial use.
 
@@ -140,11 +140,11 @@ This dataset is ready for commercial/non-commercial use.
 from datasets import load_dataset
 
 # Load the entire dataset
-dataset = load_dataset("Dataseeds/GuruShots-Sample-Dataset-GSD")
+dataset = load_dataset("Dataseeds/DataSeeds-Sample-Dataset-DSD")
 
 # Load specific split
-train_data = load_dataset("Dataseeds/GuruShots-Sample-Dataset-GSD", split="train")
-val_data = load_dataset("Dataseeds/GuruShots-Sample-Dataset-GSD", split="validation")
+train_data = load_dataset("Dataseeds/DataSeeds-Sample-Dataset-DSD", split="train")
+val_data = load_dataset("Dataseeds/DataSeeds-Sample-Dataset-DSD", split="validation")
 
 # Access images and annotations
 sample = dataset["train"][0]
@@ -167,7 +167,7 @@ from torch.utils.data import DataLoader
 import torch
 
 # Load dataset
-dataset = load_dataset("Dataseeds/GuruShots-Sample-Dataset-GSD", split="train")
+dataset = load_dataset("Dataseeds/DataSeeds-Sample-Dataset-DSD", split="train")
 
 # Convert to PyTorch format
 dataset.set_format(type="torch", columns=["image", "image_title", "segmentation_masks"])
@@ -183,7 +183,7 @@ import tensorflow as tf
 from datasets import load_dataset
 
 # Load dataset
-dataset = load_dataset("Dataseeds/GuruShots-Sample-Dataset-GSD", split="train")
+dataset = load_dataset("Dataseeds/DataSeeds-Sample-Dataset-DSD", split="train")
 
 # Convert to TensorFlow Dataset
 tf_dataset = dataset.to_tf_dataset(
@@ -201,7 +201,7 @@ tf_dataset = dataset.to_tf_dataset(
 
 ## Benchmark Results
 
-To validate the impact of data quality, we fine-tuned two state-of-the-art vision-language models—**LLaVA-NEXT** and **BLIP2**—on the GSD scene description task. We observed consistent and measurable improvements over base models:
+To validate the impact of data quality, we fine-tuned two state-of-the-art vision-language models—**LLaVA-NEXT** and **BLIP2**—on the DSD scene description task. We observed consistent and measurable improvements over base models:
 
 ### LLaVA-NEXT Results
 
@@ -223,7 +223,7 @@ These improvements demonstrate the dataset's value in improving scene understand
 
 ## Use Cases
 
-The GSD is perfect for fine-tuning multimodal models for:
+The DSD is perfect for fine-tuning multimodal models for:
 
 * **Image captioning** - Rich human-written descriptions
 * **Scene description** - Technical photography analysis
@@ -234,7 +234,7 @@ The GSD is perfect for fine-tuning multimodal models for:
 
 ## Commercial Dataset Access & On-Demand Licensing
 
-While the GSD is being released under an open source license, it represents only a small fraction of the broader commercial capabilities of the GuruShots ecosystem.
+While the DSD is being released under an open source license, it represents only a small fraction of the broader commercial capabilities of the GuruShots ecosystem.
 
 GuruShots operates a live, ongoing photography game that has amassed over 100 million images in its catalog, sourced from both amateur and professional photographers participating in thousands of themed challenges across diverse geographic and stylistic contexts. Unlike most public datasets, this corpus is: