PixlHub integrates advanced machine learning models directly into the annotation workflow to accelerate the labeling process. These tools allow for complex annotations to be generated with minimal human input while maintaining high precision.
All AI models within PixlHub are modified for optimized performance and run locally. The platform does not send data to external APIs or third-party servers. This ensures that even when using AI-assisted tools, sensitive data remains entirely within the local environment.
The following models are accessible via the Smart Tool Palette:1. Polygon from BBox This tool combines the speed of a bounding box with the precision of a polygon.
Workflow: The user draws a standard box around an object.
AI Action: The model instantly identifies the object’s boundaries within that box and converts it into a high-fidelity Polygon. This is ideal for quickly segmenting objects with complex edges.
2. Auto-Detect Objects (Local YOLO) Designed for high-volume initial labeling, this tool can populate an entire image with annotations in a single click.
Workflow: Activate the tool with one click on the canvas.
AI Action: Utilizing a modified YOLO model, the platform detects all recognizable objects in the frame and automatically labels them as Bounding Boxes based on the selected label schema.
3. Smart Segment (Local SAM) This tool allows for complex segmentation with zero manual tracing.
Workflow: The user places a single Point on the target object.
AI Action: Powered by a modified Segment Anything Model (SAM), the AI identifies the entire structure of the object and wraps it in a precise Polygon. It is the most efficient way to label irregular shapes with a single click.
AI-generated labels are treated as standard annotations. Once the AI provides a prediction, annotators can manually refine, resize, or delete the results just like any other label. This “Human-in-the-loop” approach ensures that speed never comes at the expense of accuracy.
