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, annotate a single keyframe and press P to propagate bounding boxes and segmentation masks across 50 to 300 sequential frames automatically using optical flow and tracking algorithms.
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 YOLOv9 or YOLO11 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 automatically provisions a private, self-contained Python 3.11 environment with CUDA-enabled PyTorch inside %LOCALAPPDATA%\SentinelLabeler\python on first launch. Train YOLOv8, YOLOv9, YOLOv10, and YOLO11 models with 1 click.
Automatic dataset splitting (train/val), anchor generation, mixed precision FP16 training, and mAP evaluation.
Download SentinelLabeler, plug in your capture card, and annotate your first dataset in minutes.