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Generating Optimized Synthetic Datasets

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Work in Progress
These notes are a work in progress and may not be complete or fully accurate. They are intended for personal use and reference. Content is added and updated regularly.

Project Goal​

To build, deploy, and manage a scalable service that uses finetuned diffusion models to generate diverse, high-fidelity synthetic images (e.g., retail products on shelves, specific industrial components in various settings) intended for augmenting training datasets for downstream computer vision tasks (like object detection or segmentation). The emphasis is on the pipeline's automation, optimization, monitoring, and the ability to control generation parameters.

Core Scenario​

  1. Users upload a set of images (e.g., product images, industrial components) via a simple web interface or API.
  2. Uses a finetuned diffusion model to generate batches of images matching these specs.
  3. erforms automated quality checks and validation on the generated images.
  4. Stores the generated dataset and associated metadata (prompts used, generation parameters, validation results) in a structured way (e.g., cloud storage bucket with metadata database/files).
  5. The generated images can be downloaded or accessed via an API.
  6. The entire process is optimized for throughput and cost, highly automated, and observable.