ML Model Assisted Labeling
To Supercharge Your Annotation Speed
Let AI Handle the Repetitive, So You Can Focus on What Matters
Labeling thousands of images manually? That’s a bottleneck. JTheta.ai lets you integrate your
own machine learning models—or use built-in smart logic—to pre-label data automatically.
It’s the perfect solution for teams who want to move fast without sacrificing control. Save time,
reduce labeling fatigue, and scale effortlessly with the help of AI.
How ML-Assisted Labeling Works in JTheta.ai
Bring Your Own Model (BYOM)
● Plug in your custom-trained model (via API or upload) to generate initial annotations on
images or image sequences.
Built-In AI Labeling Engines
● Use JTheta.ai’s pre-configured AI models for basic object detection, semantic
segmentation, and more.
Editable AI Outputs
● Pre-labeled regions appear in your workspace — ready for quick review, edits, or
approval by human annotators.
Supports Iterative Labeling Loops
● Feed updated labels back into your model, improve predictions, and relabel at scale —
ideal for active learning workflows.
Benefits of Using ML Model Assistance
- Massive Time Savings - Cut annotation time by up to 60%
- Higher Labeling Consistency - Reduce human error and bias
- AI + Human-in-the-Loop - Maintain quality with reviewer validation
- Scalable Data Generation - Easily process tens of thousands of images weekly
Who Should Use ML-Assisted Labeling?
Common Use Cases
Pre-labeling object bounding boxes for urban mobility AI
Auto-segmenting tumors in medical scans
AI-assisted polygon masks for satellite detection
Large-scale ecommerce or retail product image tagging
Pre-labeling pose or hand landmark datasets
Start Automating Your Labeling in Minutes
FAQs
→ Yes, we support custom model integration for pre-labeling via API or direct upload.
→ 100%. You can review, adjust, or override any AI-generated labels.
→ Models for detection, segmentation, or classification are supported — depending on your
labeling type.