Dulce™ — Batch Image Classification for Windows®
Run supported image classifiers on Windows® — without building the operator application
Run recurring batches of prepared images with supported ONNX classifiers using CPU, DirectML, or optional NVIDIA® CUDA acceleration.
Free CPU image classification.Upgrade to Standard for DirectML and CUDA GPU acceleration.
PRODUCT
Run the classifier without building the surrounding operator application
Dulce™ provides a Windows® application for running supported, separately supplied ONNX image classifiers against prepared image batches. A model provider, engineering team, or integrator can configure the classifier's required execution and preprocessing settings for use by a downstream Windows® user without requiring a purpose-built inference application, Linux server, cloud inference service, container stack, or Python environment solely to run recurring batches.
Keep images under your control
Run supported batch classification locally on Windows® hardware without a required cloud inference path.
Put your workstation to work
Run on CPU with Dulce Free. Dulce Standard adds supported DirectML and NVIDIA CUDA GPU execution, including concurrent execution across selected devices.
Review results after the run
Retain structured batch inference results and run evidence for comparison, investigation, and later analysis
Choose your Dulce™ edition
| Feature | Dulce Free | Dulce Standard |
|---|---|---|
| CPU classification | ✅ | ✅ |
| DirectML GPU | — | ✅ |
| NVIDIA CUDA GPU | — | ✅ |
| Concurrent CPU/GPU | — | ✅ |
| Batch CSV results | ✅ | ✅ |
| Run summaries and telemetry | ✅ | ✅ |
| Local processing | ✅ | ✅ |
Download Dulce Free from Microsoft Store. Upgrade to Standard directly within the application when GPU acceleration is needed.
Start with Dulce™ Free
Run CPU batch image classification at no cost. Upgrade to Standard within Dulce™ when you need GPU execution.
USE CASES
Where Dulce™ fits
You need someone else to run the classifier — Use Dulce™ when a model owner, engineering team, or integrator needs to hand a supported classifier and its known preparation recipe to another Windows® user who will run recurring image batches.
You already have images and a supported model — Use Dulce™ when you already have a prepared image set and a supported discrete-class ONNX model and need a controlled Windows® batch-classification workflow.
You want to keep inference local — Run image classification on customer-controlled Windows® hardware without requiring a cloud inference path or Internet-connected runtime.
You want to replace ad hoc batch tooling — Move from scripts or manually assembled inference workflows to a structured application that records per-image results, run-level information, and execution evidence.
You want reviewable results after the run — Use Dulce™ when classification results need to remain available for later review, documentation, investigation, or downstream analysis rather than existing only as transient runtime output.
A supported ONNX model is only part of the execution contract. The model owner must also know the class labels and order, required image preparation, and output interpretation used by the classifier.Dulce™ works with previously captured image files. It does not provide model training, camera control, PLC integration, object detection, segmentation, metrology, or real-time production-line control.
Manufacturing is one application
Typical uses include evaluating classification models against captured production images, re-examining historical image sets, comparing model behavior across datasets, and performing offline analysis of manufacturing imagery. These are examples of the broader Dulce™ workflow: running supported classifiers against prepared image batches when repeatable Windows® execution is needed.
WINDOWS® + LOCAL
Batch inference without a separate AI infrastructure stack
Stay local
Images and inference workloads remain on the workstation rather than requiring a cloud inference service. Runtime operation does not require Internet or cloud connectivity.
Use existing hardware
CPU, DirectML, and optional NVIDIA CUDA execution allow Dulce™ to use hardware already available in the workstation. GPU execution requires Standard version.
Use Windows® directly
Dulce™ runs as a Windows® desktop application without requiring operators to work with Linux, containers, or Python environments.
Keep the workflow simple
Models, image sets, execution settings, and batch results are managed through the application rather than assembled from command-line tools and scripts.
RESULTS
Reviewable batch inference output
Dulce™ records classification results and run-level information so completed batch runs can be reviewed, documented, and analyzed after processing.
Per-image results
Review classification results for the images processed during the run instead of relying only on transient runtime output.
Run-level summary
See run-level information including image counts, timestamps, throughput, and the locations of generated result artifacts.
Execution evidence
Dulce™ records per-device execution telemetry together with the model SHA-256 and, when applicable, the label-file SHA-256.
Results that remain useful after the run
Generated results can support later review, investigation, documentation, or downstream analysis using the artifacts produced by Dulce™.
Examples from a completed run:
Completed run summary
Per-image classification results
Machine-readable run summary
System requirements
Dulce requires Windows 11 x64 and a supported ONNX image classification model.
Windows® 11
64-bit Windows® 11 desktop or workstation
Execution hardware
CPU, DirectML, and optional NVIDIA® CUDA acceleration.The Free version uses CPU and the Standard version adds DirectML/CUDA.
Storage and memory
About 1.01 GB installed, with approximately 1.5–1.6 GB typically observed during batch inference
OPTIONAL NVIDIA® CUDA
CUDA acceleration
CUDA acceleration is optional. Dulce™ can run using CPU or DirectML without NVIDIA® CUDA dependencies.*
CUDA is a Standard-only feature.
| CUDA 12 | cuDNN 9 |
|---|---|
| A compatible NVIDIA® GPU, NVIDIA driver, and CUDA 12 runtime are required | cuDNN 9 runtime required and configured in Dulce Settings |
| CUDA downloads | cuDNN downloads |
* Dulce™ does not install or update NVIDIA CUDA dependencies
CUDA optional setup
DOCUMENTATION
User Guide
The Dulce™ User Guide covers installation, supported models and image inputs, CPU/DirectML/CUDA execution, CUDA requirements, batch operation, persistent data, and uninstall behavior
Support & Feedback
Contact Dulce™
Questions about Dulce installation, licensing, supported models, CUDA configuration, or application behavior can be submitted using the contact form.Please include a brief description of what you were doing and what happened. We may contact you for additional technical details if needed.Please do not include proprietary, confidential, or sensitive information in the form. If diagnostic files or other information would be useful, we will request them separately.See Privacy for information about how submitted information is handled
PRIVACY
Privacy and data practices
Dulce™ application
Dulce™ is designed for local, on-premises operation. Images are processed on the user's Windows workstation. Classification results, run summaries, and runtime telemetry are stored locally on that workstation.Dulce does not transmit images, classification results, or application-generated runtime telemetry to Sweet Software Practices Corporation.Microsoft Store separately manages application installation, updates, purchases, and licensing.
Website
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Privacy questions
Use the Support contact form
SWEET SOFTWARE PRACTICES CORPORATION
Dulce™
Windows® batch inference for manufacturing
Oshkosh, WI USA
© 2026 Sweet Software Practices Corporation
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