---
readme: "1.0.0"
dataset_info:
  features:
  - name: image
    dtype: image
  - name: url
    dtype: string
  - name: seed
    dtype: string
  splits:
  - name: train
    num_bytes: 1099511627776
    num_examples: 500000000
configs:
- config_name: default
  data_files:
  - split: train
    path: "data/*.parquet"
license: mit
task_categories:
- text-to-image
- image-classification
- unconditional-image-generation
tags:
- synthetic
- random-seeds
- 1TB
- 100TB-roadmap
- high-resolution
- open-source
- vision-core
pretty_name: Open Pixel 1T (Visual Atlas)
size_categories:
- 1B<n<10B
---

# 🌌 Open-Pixel-1T (Visual Atlas)

<div align="center">
  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="200"/>
  <br>
  <b>A Large-Scale, High-Entropy Synthetic Image Dataset for Foundational Pre-Training</b>
  <br>
  <br>
  <a href="https://huggingface.co/datasets/LAYEK-143/Open-Pixel-1T/viewer/default/train"><img src="https://img.shields.io/badge/Dataset-Viewer-green?style=for-the-badge"></a>
  <a href="https://huggingface.co/datasets/LAYEK-143/Open-Pixel-1T"><img src="https://img.shields.io/badge/Task-Vision-blue?style=for-the-badge"></a>
<a href="https://huggingface.co/datasets/LAYEK-143/Open-Pixel-1T"><img src="https://img.shields.io/badge/License-MIT-red?style=for-the-badge"></a>
<a href="https://huggingface.co/datasets/LAYEK-143/Open-Pixel-1T"><img src="https://hitscounter.dev/api/hit?url=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FLAYEK-143%2FOpen-Pixel-1T&label=VIEWS&icon=eye&color=%23ffc107&message=&style=for-the-badge&tz=UTC" alt="VIEWS"></a>
</div>

---

## 📑 Dataset Summary

**Open-Pixel-1T** is a monumental open-source initiative designed to create a "Visual Atlas" of stochastic imagery. Unlike traditional datasets scraped from social media which contain inherent human bias, Open-Pixel-1T is constructed using high-entropy random seeds to generate unique, diverse visual signals.

This dataset serves as a **foundational layer** for computer vision research, specifically targeting self-supervised learning (SSL), variational autoencoders (VAEs), and large-scale generative pre-training where data volume and variance are critical.

### 🚀 Roadmap & Scale
The project follows an aggressive expansion roadmap:
* **Phase 1 (Current):** 2 Terabyte (2TB) of high-resolution data.
* **Phase 2:** Expansion to 10 Terabytes (10TB).
* **Phase 3:** Long-term goal of **100 Terabytes (100TB)** of open visual data.

### 🎯 Key Specifications
* **Resolution:** Standardized **1024x1024** px.
* **Format:** Optimized **Apache Parquet** (Snappy Compression).
* **Source:** Synthetic randomness via UUIDv4 seeding (Picsum Source).
* **Entropy:** Maximized randomness to prevent overfitting on specific visual domains.

---

## 💾 Dataset Structure

The dataset is sharded into ~1GB Parquet files to facilitate distributed training and streaming. Each row represents a unique image sample generated from a unique seed.

### Data Fields

| Field | Type | Description |
| :--- | :--- | :--- |
| **`image`** | `image` | The raw image binary (PIL compatible). |
| **`url`** | `string` | The source URL containing the unique seed used for generation. |
| **`seed`** | `string` | The UUIDv4 seed key responsible for the image's visual output. |

### Sample Data
```json
{
  "image": "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x1024>",
  "url": "[https://picsum.photos/seed/a1b2-c3d4-e5f6/1024/1024](https://picsum.photos/seed/a1b2-c3d4-e5f6/1024/1024)",
  "seed": "a1b2-c3d4-e5f6"
}
```

---

## 🛠️ Usage

### 1. Streaming (Recommended)
Due to the massive size (1TB+), it is recommended to stream the dataset rather than download it entirely.

```python
from datasets import load_dataset

# Stream the dataset (No disk space required)
dataset = load_dataset("LAYEK-143/Open-Pixel-1T", split="train", streaming=True)

# Iterate through images
for i, sample in enumerate(dataset):
    print(f"Processing image {i}: {sample['seed']}")
    image = sample['image']
    image.show()
    
    if i == 5: break
```

### 2. PyTorch DataLoader Integration
The dataset is optimized for high-throughput training pipelines.

```python
import torch
from torch.utils.data import DataLoader
from datasets import load_dataset
from torchvision import transforms

# Define transforms
transform_pipeline = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
])

def process_batch(examples):
    examples["pixel_values"] = [transform_pipeline(img.convert("RGB")) for img in examples["image"]]
    return examples

# Load in streaming mode
dataset = load_dataset("LAYEK-143/Open-Pixel-1T", split="train", streaming=True)
dataset = dataset.map(process_batch, batched=True, remove_columns=["image", "url", "seed"])

# Create Loader
dataloader = DataLoader(dataset, batch_size=64)
```

---

## ⚖️ Citation & License

### License
This dataset is released under the **MIT License**. You are free to use it for research, commercial, and open-source projects.

### Citation
If you use this dataset in your research or project, please cite it as:

```bibtex
@dataset{open_pixel_1t,
  author       = {Ryan Shelby},
  title        = {Open-Pixel-1T: A Large-Scale Synthetic Visual Atlas},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{[https://huggingface.co/datasets/LAYEK-143/Open-Pixel-1T](https://huggingface.co/datasets/LAYEK-143/Open-Pixel-1T)}},
  note         = {Targeting 100TB of open visual data}
}
```

---

<div align="center">
  Created with ❤️ by <b>Ryan Shelby</b> | 2026
</div>