Use state-of-the-art AI foundation models to generate initial annotations, then have human annotators review and correct for high-quality training data.
Available Foundation Models for Pre-Labeling
- SAM (Segment Anything) - Zero-shot image segmentation for any object
- CLIP - Zero-shot image classification and visual similarity
- YOLO v8 - Real-time object detection pre-labels
- GPT-4 / Claude - Text classification, NER, and sentiment analysis
- Whisper - Automatic speech recognition and transcription
AI-Assisted Labeling Workflow
- Enable pre-labeling in your TigerLabel project settings
- Select foundation model and configure confidence thresholds
- Run batch inference on your unlabeled data
- Annotators review, correct, and approve AI-generated labels
- Track pre-label accuracy to optimize model selection
Pro Tip: AI pre-labeling typically reduces annotation time by 50-80% while maintaining human-verified quality. Start with high-confidence predictions for maximum efficiency.