Every tool inside SentinelLabeler is designed to solve the real challenges of high-framerate video labeling: small moving targets, rapid motion blur, lighting shifts, and massive frame counts.
Generating pixel-perfect polygon masks by hand is excruciatingly slow. With SentinelLabeler's local SAM 2.1 integration, simply click once on any character, helmet, or vehicle to generate an exact polygon outline instantly. Click background areas to add negative prompt points and refine tight edges in seconds.
Click target → SAM 2.1 computes feature embedding → instant polygon vertices and YOLO annotations.
Video footage contains hundreds of continuous frames where targets move predictably. Rather than re-drawing boxes frame by frame, label one frame, select a box and press T to track it forward through up to 60 frames with optical flow, or track every object in the frame at once with an AI tracker.
Optical flow velocity estimation keeps bounding boxes anchored onto moving targets across dynamic scenes.
Standard neural network inference downscales high-resolution 1080p, 1440p, or 4K video frames into 640x640, often obliterating distant small targets into single unrecognizable pixels. SAHI slices the frame into overlapping tiles, runs auto-annotation on each high-res crop, and merges detections seamlessly with non-maximum suppression (NMS).
High-density crops detect 8px–20px targets that full-frame CNN architectures completely discard.
Wondering whether your newly trained model actually outperforms your baseline on real game capture footage? SentinelLabeler runs two models simultaneously on the live feed, displaying bounding boxes and confidence scores side by side in real time.
Compare precision, recall, and edge adherence live before deploying models to your runtime engine.
No need to configure CUDA paths, virtual environments, or deal with broken PyTorch wheels. SentinelLabeler installs its own private Python 3.12 with PyTorch on first launch, separate from anything else on your PC. Train YOLO11, YOLO26 and RT-DETR models with one click.
Automatic dataset splitting (train/val), anchor generation, mixed precision FP16 training, and mAP evaluation.
Bring your existing models into SentinelLabeler instantly. The Live Pre-Label Assist engine automatically parses and inspects any ONNX detector — from single-class YOLOv8 models to multi-class YOLO11 and YOLO26 architectures. Embedded class labels and tensor topologies ([1, 5, 8400], [1, 84, 8400]) are normalized on-the-fly, auto-adopting missing classes into your project schema with zero manual config edits.
Drop your model → Live Pre-Label Assist auto-binds outputs and classes → instant auto-annotation.
Download SentinelLabeler, plug in your capture card, and annotate your first dataset in minutes.