HiHat AI

Upload raw sensor data.

Run Auto-Refinement for vehicle and pedestrian detection.

Review high-uncertainty frames in the 3D workspace.

Export in TFRecord for training.

Upload DICOM files.

Use AI-assisted segmentation to suggest tumor boundaries.

Radiologist verifies and adjusts the masks.

System versions the dataset for clinical trial tracking.

Connect HiHat to product image S3 bucket.

Define natural language prompts for VLM-based pre-labeling.

Automated sorting into hierarchical categories.

Human spot-check of 5% of the data to verify accuracy.

Import multispectral drone images.

Apply polygon annotation for specific plant types.

Use active learning to find rare weed species.

Export labels to update edge-computing models on drones.

Ingest live RTSP streams.

Auto-label temporal events (e.g., accidents).

Annotators confirm event boundaries.

Retrain detection model to reduce false alarms.

Upload prompt-response pairs.

Labelers rank responses based on custom rubrics.

HiHat calculates consensus between multiple labelers.

Download ranked dataset for preference optimization.

Import GeoTIFF satellite files.

Use the 'Change Detection' tool to highlight differences between frames.

Labelers verify building footprints.

Export as GeoJSON.