# Auto Labeling Guide **Bootstrapping Datasets with Auto Labeling** **Level:** Intermediate | **Estimated Time:** 30-45 minutes | **Tags:** Auto-Labeling, Annotation, Delegated Operations, Model Inference This guide walks you through using FiftyOne’s Auto Labeling to rapidly bootstrap and refine labels on your dataset. You’ll learn how to: - Generate high-quality auto labels using state-of-the-art models - Analyze and review predictions with confidence-based filtering - Refine your labels with visualization tools (patches view, embeddings) - Systematically approve correct predictions and flag issues - Complete the auto labeling workflow to integrate labels into your dataset ## Guide Overview Auto Labeling combines model inference with human verification to dramatically accelerate dataset labeling. The workflow consists of the following steps: 1. **Gather Your Data and Infrastructure** - Prepare your dataset in FiftyOne and configure GPU orchestration 2. **Configure Auto Labeling Run** - Configure and launch an auto labeling task and track progress 3. **Analyze Results** - Review predictions and select samples for approval 4. **Visualize Embeddings** - Generate patch embeddings and use it to analyze clusters of samples 5. **Finalize Workflow** - Accept approved labels and discard problematic predictions ## Prerequisites #### NOTE Auto Labeling is available in [FiftyOne Enterprise](../../enterprise/index.md#fiftyone-enterprise). If you are using open source FiftyOne and are interested in this feature, please reach out to [Voxel51 sales](https://voxel51.com/sales). **Who Is This Guide For** This guide is designed for machine learning practitioners and data scientists who need to efficiently label large datasets. Whether you’re bootstrapping a new project or improving existing annotations, Auto Labeling provides a systematic approach to leveraging model predictions while maintaining human oversight. **Required Knowledge** - Familiarity with the FiftyOne Enterprise App interface and basic operations - Understanding of your target task (detection, classification, segmentation) - Basic knowledge of machine learning models and confidence thresholds **System Requirements** - **FiftyOne Enterprise:** This feature requires FiftyOne Enterprise with delegated operations - **GPU Access:** Orchestrator must have GPU resources for model inference - **Storage:** Sufficient object storage space for dataset media and label fields ## Ready to Begin? Click **Next** to start with the first step: Prepare Your Dataset and Delegated Operators.