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| Udvikler: | Linn Lim (6) | ||
| Pris: | ▼ $4.99 | ||
| Vurdering: | 960 Sølv | ||
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| +1 Point | |||
Beskrivelse
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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«AnomalyKit». Platform: Windows. Kategori: Udviklerværktøjer. Udvikler: «Linn Lim». Første udgivelse: . Sidste opdatering: . Tidligere pris: $9.99. Nuværende pris: $4.99 (50% rabat). Laveste registrerede pris: $4.99 (). Højeste registrerede pris: $9.99 (). Denne titel har endnu ikke modtaget vurderinger eller anmeldelser på AppAgg. Tilgængelige sprog: English. AppAgg sporer prishistorik, vurderinger og brugerfeedback for «AnomalyKit». AppAgg hoster ikke applikationer eller distribuerer software. Alle varemærker, logoer og skærmbilleder tilhører deres respektive ejere. Følg fremtidige rabatter og opdateringer via RSS
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