momanz1/gfd

DataSeeds.AI Sample Dataset (DSD)

Dataset Summary

The DataSeeds.AI Sample Dataset (DSD) is a high-fidelity, human-curated computer vision-ready dataset comprised of 7,772 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 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 - Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery
• Github Repo - Access the complete weights and code which were used to evaluate the DSD -- https://github.com/DataSeeds-ai/DSD-finetune-blip-llava

This dataset is ready for commercial/non-commercial use.

Dataset Structure

• Size: 7,772 images (7,010 train, 762 validation)
• Format: Apache Parquet files for metadata, with images in JPG format
• Total Size: ~4.1GB
• Languages: English (annotations)
• Annotation Quality: All annotations were verified through a multi-tier human-in-the-loop process

Data Fields

Column Name	Description	Data Type
image_id	Unique identifier for the image	string
image	Image file, PIL type	image
image_title	Human-written title summarizing the content or subject	string
image_description	Human-written narrative describing what is visibly present	string
scene_description	Technical and compositional details about image capture	string
all_labels	All object categories identified in the image	list of strings
segmented_objects	Objects/elements that have segmentation masks	list of strings
segmentation_masks	Segmentation polygons as coordinate points [x,y,...]	list of lists of floats
exif_make	Camera manufacturer	string
exif_model	Camera model	string
exif_f_number	Aperture value (lower = wider aperture)	string
exif_exposure_time	Sensor exposure time (e.g., 1/500 sec)	string
exif_exposure_mode	Camera exposure setting (Auto/Manual/etc.)	string
exif_exposure_program	Exposure program mode	string
exif_metering_mode	Light metering mode	string
exif_lens	Lens information and specifications	string
exif_focal_length	Lens focal length (millimeters)	string
exif_iso	Camera sensor sensitivity to light	string
exif_date_original	Original timestamp when image was taken	string
exif_software	Post-processing software used	string
exif_orientation	Image layout (horizontal/vertical)	string

How to Use

Basic Loading

from datasets import load_dataset

# Load the training split of the dataset
dataset = load_dataset("Dataseeds/DataSeeds.AI-Sample-Dataset-DSD", split="train")

# Access the first sample
sample = dataset[0]

# Extract the different features from the sample
image = sample["image"]  # The PIL Image object
title = sample["image_title"]
description = sample["image_description"]
segments = sample["segmented_objects"]
masks = sample["segmentation_masks"] # The PIL Image object for the mask

print(f"Title: {title}")
print(f"Description: {description}")
print(f"Segmented objects: {segments}")

PyTorch DataLoader

from datasets import load_dataset
from torch.utils.data import DataLoader
import torch

# Load dataset
dataset = load_dataset("Dataseeds/DataSeeds.AI-Sample-Dataset-DSD", split="train")

# Convert to PyTorch format
dataset.set_format(type="torch", columns=["image", "image_title", "segmentation_masks"])

# Create DataLoader
dataloader = DataLoader(dataset, batch_size=16, shuffle=True)

TensorFlow

import tensorflow as tf
from datasets import load_dataset

TARGET_IMG_SIZE = (224, 224)
BATCH_SIZE = 16
dataset = load_dataset("Dataseeds/DataSeeds.AI-Sample-Dataset-DSD", split="train")

def hf_dataset_generator():
    for example in dataset:
        yield example['image'], example['image_title']

def preprocess(image, title):
    # Resize the image to a fixed size
    image = tf.image.resize(image, TARGET_IMG_SIZE)
    image = tf.cast(image, tf.uint8)
    return image, title

# The output_signature defines the data types and shapes
tf_dataset = tf.data.Dataset.from_generator(
    hf_dataset_generator,
    output_signature=(
        tf.TensorSpec(shape=(None, None, 3), dtype=tf.uint8),
        tf.TensorSpec(shape=(), dtype=tf.string),
    )
)

# Apply the preprocessing, shuffle, and batch
tf_dataset = (
    tf_dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)
    .shuffle(buffer_size=100)
    .batch(BATCH_SIZE)
    .prefetch(tf.data.AUTOTUNE)
)

print("Dataset is ready.")
for images, titles in tf_dataset.take(1):
    print("Image batch shape:", images.shape)
    print("A title from the batch:", titles.numpy()[0].decode('utf-8'))

Dataset Characterization

Data Collection Method: Manual curation from GuruShots photography platform

Labeling Method: Human annotators with multi-tier verification process

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 DSD scene description task. We observed consistent and measurable improvements over base models:

LLaVA-NEXT Results

Model	BLEU-4	ROUGE-L	BERTScore F1	CLIPScore
Base	0.0199	0.2089	0.2751	0.3247
Fine-tuned	0.0246	0.2140	0.2789	0.3260
Relative Improvement	+24.09%	+2.44%	+1.40%	+0.41%

BLIP2 Results

Model	BLEU-4	ROUGE-L	BERTScore F1	CLIPScore
Base	0.001	0.126	0.0545	0.2854
Fine-tuned	0.047	0.242	-0.0537	0.2583
Relative Improvement	+4600%	+92.06%	-198.53%	-9.49%

These improvements demonstrate the dataset's value in improving scene understanding and textual grounding of visual features, especially in fine-grained photographic tasks.

Use Cases

The DSD is perfect for fine-tuning multimodal models for:

• Image captioning - Rich human-written descriptions
• Scene description - Technical photography analysis
• Semantic segmentation - Pixel-level object understanding
• Aesthetic evaluation - Style classification based on peer rankings
• EXIF-aware analysis - Technical metadata integration
• Multimodal training - Vision-language model development

Commercial Dataset Access & On-Demand Licensing

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.

DataSeeds.AI operates a live, ongoing photography catalog that has amassed over 100 million images, 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:

• Fully licensed for downstream use in AI training
• Backed by structured consent frameworks and traceable rights, with active opt-in from creators
• Rich in EXIF metadata, including camera model, lens type, and occasionally location data
• Curated through a built-in human preference signal based on competitive ranking, yielding rare insight into subjective aesthetic quality

On-Demand Dataset Creation

Uniquely, DataSeeds.AI has the ability to source new image datasets to spec via a just-in-time, first-party data acquisition engine. Clients (e.g. AI labs, model developers, media companies) can request:

• Specific content themes (e.g., "urban decay at dusk," "elderly people with dogs in snowy environments")
• Defined technical attributes (camera type, exposure time, geographic constraints)
• Ethical/region-specific filtering (e.g., GDPR-compliant imagery, no identifiable faces, kosher food imagery)
• Matching segmentation masks, EXIF metadata, and tiered annotations

Within days, the DataSeeds.AI platform can launch curated challenges to its global network of contributors and deliver targeted datasets with commercial-grade licensing terms.

Sales Inquiries

To inquire about licensing or customized dataset sourcing, contact: sales@dataseeds.ai

License & Citation

License: Apache 2.0

For commercial licenses, annotation, or access to the full 100M+ image catalog with on-demand annotations: sales@dataseeds.ai

Citation

If you find the data useful, please cite:

@article{abdoli2025peerranked,
    title={Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from GuruShots' Annotated Imagery}, 
    author={Sajjad Abdoli and Freeman Lewin and Gediminas Vasiliauskas and Fabian Schonholz},
    journal={arXiv preprint arXiv:2506.05673},
    year={2025},
}