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| Developer: | Linn Lim (4) | ||
| Price: | $9.99 | ||
| Score: | 960 Silver | ||
| Rankings: | 0 | ||
| Comments: | 0 Add a comment | ||
| Lists: | 0 + 0 | ||
| Points: | 0 + 0 ¡ | ||
| +1 Points | |||
Description
Train a custom detector on your own image folders in minutes. The trainer automatically calibrates a working threshold, exports a portable ONNX model, and writes everything you need so the model is production-ready the moment training finishes. No separate calibration step, no Python environment to set up, no glue code to write.
Run inference with ONNX Runtime — fast, portable, and PyTorch-free at predict time. Score thousands of images, view side-by-side heatmap overlays that show exactly where each anomaly is, and export verdicts to CSV with one click.
For QA and manufacturing teams — point AnomalyKit at a folder of "good" example images plus a folder of known defects, and you get a working visual inspector. The calibration tool helps you pick conservative thresholds for high-recall inspection or aggressive thresholds when false alarms matter. Models exported by AnomalyKit can be integrated into existing line software via ONNX Runtime.
For ML engineers and researchers — AnomalyKit ships a faithful implementation of SimpleNet (CVPR 2023) with WideResNet50 and ResNet50 backbones, full PyTorch 2.12 training pipeline, ONNX export with verification, and built-in support for MVTec-style dataset layouts. Reproduce paper benchmarks, evaluate against your own data, or use the underlying scripts directly.
What's included:
SimpleNet Train & Eval — full training pipeline with CUDA, auto-calibration, ONNX export
Predict Anomalies — ONNX inference with heatmap visualization and CSV export
Threshold Calibration — histogram analysis, ROC/F1 metrics, three operating points (optimal, conservative, aggressive)
Supervised Classifier Suite — binary good/defect classification, separate from the unsupervised SimpleNet path
Dataset Builder — guided folder-structure creation for MVTec-style training
Binary Mask Annotator — paint defect regions for supervised pipelines
Environment Health — diagnostics for the bundled stack
AnomalyKit bundles a complete Python environment: PyTorch 2.12 with CUDA 13, ONNX Runtime with DirectML, and every dependency needed for both training and inference. Install once and run — no separate setup, no version conflicts, no missing DLLs.
Built for Windows 10 and Windows 11. NVIDIA GPU recommended for training; CPU and integrated graphics work for inference. Fully offline, with no telemetry or network calls of any kind.
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AppAgg Score
960
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AnomalyKit
960 Silver
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Short URL: Copied!
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- Version: PC
- Last Updated:
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- Category:
Windows Apps›Developer tools - OS:
PC - Size:
2 Gb - Supported Languages:
English - Content Rating:
EVERYONE - Windows Store Rating:
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Developer
- Linn Lim
- Platforms: Windows Apps (4)
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Summary
AnomalyKit Short URL: Copied!- Share
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«AnomalyKit» is a Developer tools app for Windows, developed by «Linn Lim». It was first released on and last updated on . The lowest historical price was $9.99, and the highest was $9.99. This app has not yet received any ratings on AppAgg. Available languages: English. AppAgg continuously tracks the price history, ratings, and user feedback for «AnomalyKit». AppAgg does not host applications or distribute software. All trademarks, logos and screenshots belong to their respective owners. Subscribe to this app or follow its RSS feed to get notified about future deals or updates.
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