Duplicate from Dataseeds/DataSeeds.AI-Sample-Dataset-DSD

Co-authored-by: IT Admind <Zedge@users.noreply.huggingface.co>

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README.md

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+---
+language:
+- en
+license: apache-2.0
+size_categories:
+- 1K<n<10K
+task_categories:
+- image-classification
+- object-detection
+- image-to-text
+tags:
+- computer-vision
+- photography
+- annotations
+- EXIF
+- scene-understanding
+- multimodal
+dataset_info:
+  features:
+  - name: image_id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: image_title
+    dtype: string
+  - name: image_description
+    dtype: string
+  - name: scene_description
+    dtype: string
+  - name: all_labels
+    sequence: string
+  - name: segmented_objects
+    sequence: string
+  - name: segmentation_masks
+    sequence:
+      sequence: float64
+  - name: exif_make
+    dtype: string
+  - name: exif_model
+    dtype: string
+  - name: exif_f_number
+    dtype: string
+  - name: exif_exposure_time
+    dtype: string
+  - name: exif_exposure_mode
+    dtype: string
+  - name: exif_exposure_program
+    dtype: string
+  - name: exif_metering_mode
+    dtype: string
+  - name: exif_lens
+    dtype: string
+  - name: exif_focal_length
+    dtype: string
+  - name: exif_iso
+    dtype: string
+  - name: exif_date_original
+    dtype: string
+  - name: exif_software
+    dtype: string
+  - name: exif_orientation
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3715850996.79
+    num_examples: 7010
+  - name: validation
+    num_bytes: 408185964.0
+    num_examples: 762
+  download_size: 4134168610
+  dataset_size: 4124036960.79
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+---
+
+# DataSeeds.AI Sample Dataset (DSD)
+
+![DSD Example](./GSD-example.jpeg)
+
+## 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](https://huggingface.co/papers/2506.05673)
+* **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)
+
+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
+
+```python
+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
+
+```python
+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
+
+```python
+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](mailto: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](mailto:sales@dataseeds.ai)
+
+### Citation
+
+If you find the data useful, please cite:
+
+```bibtex
+@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},
+}
+```

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