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| 개발자: | Linn Lim (6) | ||
| 가격: | ▼ $4.99 | ||
| 평가: | 960 실버 | ||
| 순위: | 0 | ||
| 댓글: | 0 댓글 게시 | ||
| 목록: | 0 + 0 | ||
| 포인트: | 0 + 0 ¡ | ||
| +1 포인트 | |||
기술
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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가격 내역
- 현재 가격: $4.99
$9.99-50% - 최소: ▼ $4.99
- 최대: $9.99
가격 추적
AppAgg 평가
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AppAgg 예상 평가:
AnomalyKit
960 실버
평가가 높을수록 보통 AppAgg에서 더 강한 신호를 의미합니다.
기준: 인기 · 사용자 평점 · 최신성 · 딜 · 스토어 데이터 · 커뮤니티 활동
최신 AppAgg 데이터를 바탕으로 매일 다시 계산됩니다.
단축 URL: 복사됨!
새로운 기능
- 버전: PC
- 마지막 업데이트:
추가 정보
개발자
- Linn Lim
- 플랫폼: Windows 앱 (5) Windows 게임 (1)
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- 포인트: 0 + 0 ¡
- 순위: 0
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- 할인: 2
- 동영상: 0
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요약
AnomalyKit 단축 URL: 복사됨!- 공유하기
- Windows Store
«AnomalyKit». 플랫폼: Windows. 카테고리: 개발자 도구. 개발자: «Linn Lim». 첫 번째 릴리스: . 마지막 업데이트: . 이전 가격: $9.99. 현재 가격: $4.99 (50% 할인). 최저 기록 가격: $4.99 (). 최고 기록 가격: $9.99 (). 이 타이틀은 아직 AppAgg에서 평가나 리뷰를 받지 못했습니다. 사용 가능한 언어: English. AppAgg는 «AnomalyKit»의 가격 기록, 평점 및 사용자 피드백을 추적합니다. AppAgg는 애플리케이션을 호스팅하거나 소프트웨어를 배포하지 않습니다. 모든 상표, 로고 및 스크린샷은 해당 소유자의 자산입니다. 향후 할인 및 업데이트 받기: RSS에서 확인할 수 있습니다
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