VIA runs entirely within a modern web browser without requiring server-side processing or installation. Once the page is loaded, it can function offline, processing data locally on the user's machine.
The tool supports annotation of not just images, but also audio and video files. For video, it allows frame-by-frame annotation and temporal segment labeling.
Users can annotate using multiple shape types (rectangles, polygons, circles, points, polylines) and define custom attributes (text, numbers, checkboxes, radio buttons) for both regions and entire files.
All annotations, project metadata, and file references are saved in a single, human-readable JSON file. This file can be easily version-controlled, shared, and processed by scripts.
While primarily a desktop tool, VIA supports collaboration by allowing project JSON files to be shared and merged. Multiple annotators can work on subsets of data and their work can be combined.
Researchers and PhD students use VIA to create ground-truth datasets for novel computer vision tasks, such as segmenting rare biological specimens or annotating historical documents. Its flexibility in defining custom attributes allows them to capture the precise labels needed for their experiments. The free, open-source nature aligns perfectly with academic budgets and the need for transparent, reproducible methodology.
Small companies or indie developers working on niche ML applications (e.g., detecting defects in custom machinery, identifying specific retail products) use VIA to manually label their proprietary image collections. They draw bounding boxes or polygons around objects of interest and assign class labels. The local processing ensures their confidential product images remain secure, and the JSON output can be directly fed into training scripts for models like YOLO or Faster R-CNN.
Medical researchers and radiologists utilize VIA to annotate regions of interest in X-rays, MRI scans, or histopathology slides, marking tumors, lesions, or anatomical structures. The ability to use polygons for precise segmentation is crucial. Data privacy is paramount, and VIA's client-side operation ensures sensitive patient data is never uploaded to the cloud, complying with strict data handling protocols common in healthcare research.
Ethologists or social scientists studying animal or human behavior in video footage use VIA to label temporal events and spatial locations frame-by-frame. They can annotate when a specific action starts and ends and mark the actor's position. This creates structured datasets for training models to automatically recognize behaviors, saving countless hours of manual observation and enabling large-scale quantitative analysis.
Instructors teaching courses on machine learning or computer vision use VIA as a hands-on tool for students to understand dataset creation. Students learn the fundamentals of annotation, label consistency, and the importance of high-quality ground truth by building a small dataset for a class project. The zero-cost and zero-setup barrier makes it ideal for classroom environments with diverse student hardware.
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