# reTraining
### Take a base model you already have, and make it measurably better with footage you already have.
**The Open-Source, Self-Hosted Vision Pipeline to Turn Raw Industrial CCTV into Production-Grade YOLO Object Detectors & Line Counters.**
[โก Quickstart](#quickstart) ยท [๐๏ธ Architecture](#architecture) ยท [๐ผ๏ธ Visual UI Tour](#visual-tour) ยท [๐ฅ Counting Engine](#counting-engine) ยท [โ๏ธ Configuration](#configuration) ยท [๐ Documentation](#documentation) ยท [๐ง Troubleshooting](#troubleshooting)
*Click diagram to view 4K UHD resolution. Scalable vector version available at [docs/diagram-alur.svg](docs/diagram-alur.svg).*
Figure 1.1: Projects Dashboard showing active projects, base models, class taxonomies, and dataset stats.
Figure 1.2: Project Creation dialog with base model checkpoint upload and automatic class extraction.
Figure 1.3: Video Archive grouping recordings into 24-hour operational shifts (06:00 to 05:59 next morning).
Figure 1.4: Video Trim interface with timeline range sliders and real-time frame calculation.
Figure 2.1: Batch Management table with batch status badges and bulk action triggers.
Figure 2.2: Single-batch SAM3 auto-annotation configuration with natural language text prompts.
Figure 2.3: Mass Auto-Annotate dialog for enqueuing thousands of frames across multiple batches.
Figure 2.4: SAM3 Interactive Playground for zero-shot text prompting and point prompt testing.
Figure 3.1: Annotation Review Canvas with bounding box editor, shape provenance badges, and hotkey controls.
Figure 3.2: Filmstrip thumbnail navigation and floating single-keystroke quick reclassification bar.
Figure 3.3: Exemplar Pool Sidebar Panel for visual reference matching.
Figure 4.1: Quality filter sliders with live box and frame retention counters.
Figure 4.2: Interactive Scatter Plot (Score vs Area) for instant visual outlier cluster detection.
Figure 4.3: Triage Crop Grid for rapid bulk visual inspection and 1-click outlier removal.
Figure 4.4: Albumentations training augmentation presets with real-time visual preview.
Figure 4.5: Dataset Freeze dialog enforcing deterministic SHA-1 Stable Val Split rules.
Figure 5.1: Master Dataset repository showing version history, train/val splits, and export tools.
Figure 5.2: YOLO fine-tuning parameter setup with hardware detection and model history.
Figure 5.3: Real-time training telemetry with live loss curves, mAP metrics, and stdout logs.
Figure 6.1: Counting Accuracy Benchmark Matrix comparing multi-model predictions against Ground Truth.
Figure 6.2: Real-time CCTV live counting interface with interactive tripwire line and ByteTrack trails.