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Creating a Synthetic Dataset

Follow these simple steps to generate your synthetic dataset using Sinkove’s platform:

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Step 1: Select Model

Navigate to the Generator tab and choose a suitable generative model from the dropdown menu. Our models are optimized for different types of medical imaging scenarios.
Different models may specialize in specific imaging modalities or medical conditions. Choose the one that best fits your research requirements.

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Step 2: Configure Dataset

Fill in the provided fields with the details of the medical images you wish to generate:
  • Prompts/Descriptions: Enter relevant medical descriptions (e.g., “Severe cardiomegaly”, “Normal chest X-ray”)
  • Dataset Size: Currently limited to approximately 1000 images
  • Additional Parameters: Configure any model-specific settings as needed
Use specific, clinical terminology in your prompts for the most accurate synthetic image generation.

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Step 3: Generate Data

You have two options for data generation:

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Preview Generation

  • Click Generate sample to preview a single synthetic image
  • Review the quality and adjust parameters if needed
  • This helps ensure the output matches your expectations

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Full Dataset Generation

  • Set your desired dataset size (up to ~1000 images)
  • Click Generate dataset to initiate full dataset generation
  • Monitor the progress in your dashboard
Dataset generation may take several minutes to hours depending on the size and complexity of your request.

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Downloading Your Dataset

Once your dataset is ready, use our Python SDK for quick and efficient downloading: 
import uuid
from sinkove import Client

# Initialize the client with your organization ID
# API key is read from SINKOVE_API_KEY environment variable
client = Client(uuid.UUID("your-organization-id"))

# Get your dataset by ID
dataset_id = uuid.UUID("your-dataset-id")
dataset = client.datasets.get(dataset_id)

# Check if dataset is ready
if dataset.ready:
    # Download the dataset
    dataset.download("./dataset.zip", strategy="replace")
    print("Dataset downloaded successfully!")
else:
    print(f"Dataset not ready. Current state: {dataset.state}")
    
    # Or wait for it to be ready
    dataset.wait()  # Blocks until ready
    dataset.download("./dataset.zip", strategy="replace")
    print("Dataset downloaded successfully!")
Need to set up the SDK? Check our SDK Installation Guide or jump straight to the SDK Quick Start.

SDK Installation

Install and configure the Python SDK

SDK Quick Start

Create your first dataset with code

API Keys

Set up API authentication

SDK Examples

Browse practical code examples

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Need Help?

If you encounter any issues or have questions:
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