AUTOFIT

An Auto-Labeling Platform that Generates Labels Just by Adding Data, AUTOFIT

Just add data, we label the rest

Scale labeling, not manpower

Fast and automated labeling
of massive datasets

Less cost, more labels

Reducing time and labor costs
compared to manual labeling

Consistent labels, reliable AI

Maintaining high labeling accuracy
and consistency

Human in the loop, AI at the wheel

Semi-automated workflow
with human inspection and correction

The more you use it, the smarter it gets

Models improve as data accumulates,
gradually enhancing label quality

Plug into your pipeline, label anywhere

Easy integration and expansion with existing
labeling tools or AI pipelines

Automatically Converts Raw Image & Video Data into Training Datasets for AI
AUTOFIT

AUTOFIT is an industry-specific tool that simultaneously boosts labeling speed and quality through AI-based auto-labeling for images and videos, key frame extraction, de-duplication, and support for bounding box and polygon types.

Beyond auto-labeling, it provides an integrated end-to-end labeling process from pre-processing to format conversion, offering superior efficiency and scalability in deployment and operation compared to generic tools.

Actual operation screen example of AUTOFIT software, featuring automated image data labeling workflow setup, bounding box and polygon object tagging diagnostic tool interface, and dataset preprocessing management.
AUTOFIT system structure diagram showing data flow from raw data in NAS via System Manager/Project Manager roles into Web Application (WAS Server) for pre-labeling scheduling, constructing raw datasets, performing labeling work/inspection by labelers and inspectors, and exporting completed data into YOLO or COCO Data Set formats, integrated with back-end GPU Server (Docker with SAM), NAS, and DB Server (MySQL).
  • Provides AI-based automated labeling for images and videos
  • Reduces overall labeling time by automating repetitive tasks
  • Decreases human error by minimizing manual intervention
  • Improves labeling quality and consistency through automation
  • Cuts labeling costs through labor and time savings
  • Efficient data usage by extracting only essential key frames from videos
  • Minimizes unnecessary labeling work via duplicate frame removal
  • Optimized for general detection tasks with Bounding Box labeling support
  • Handles complex object shapes with Polygon type labeling support
  • Provides a labeling environment for integrated management and processing of image/video data

Convenient image data labeling is possible
with AUTOFIT.