# Summary: What You’ve Learned You’ve completed the Annotation Getting Started Guide. Here’s what you can now do. ## Quickstart Track You learned the basics of multimodal annotation: - Load grouped datasets with synchronized 2D/3D data - Switch between camera slices and point clouds - Enter Annotate mode (sample modal -> Annotate tab) - Create label fields with enforced schemas - Draw bounding boxes on images - Explore point cloud data in the 3D visualizer ## Full Loop Track You built a complete data-centric annotation pipeline for multimodal data: **Step 2: Setup Splits** : Cloned quickstart-groups to `annotation_tutorial`. Created test (frozen), val (iteration), golden (QA), and pool splits **at the group level** to prevent data leakage across modalities. **Step 3: Smart Selection** : Used ZCore diversity scoring on camera images to select high-coverage scenes. Better than random. **Step 4: 2D Annotation** : Labeled detections on left camera images with KITTI schema enforcement. Only samples with actual labels get marked as annotated. **Step 5: 3D Annotation** : Annotated cuboids on point clouds using transform controls. Used camera projections to verify 3D→2D alignment. **Step 6: Train + Evaluate** : Trained YOLOv8 on camera images, evaluated on val set, tagged FP/FN failures for targeting. **Step 7: Iteration** : Ran Golden QA check, then selected next batch using hybrid strategy: 30% coverage + 70% targeted. ## Key Takeaways 1. **Group-level splits are non-negotiable.** Without them, the same scene leaks between train and test. 2. **Label smarter, not harder.** Diversity sampling + failure targeting beats random selection. 3. **30% coverage budget matters.** Only chasing failures creates a model that fails on normal cases. 4. **Cross-modal consistency.** 2D and 3D labels should agree on the same objects. 5. **QA before training.** Golden QA checks catch annotation drift early. 6. **Understand your failures.** FP/FN analysis tells you what to label next. ## When to Use What **In-app annotation is good for:** - Small to medium tasks (tens to hundreds of samples) - Multimodal data with linked 2D/3D views - Quick corrections and QA passes - Prototyping label schemas - Single annotator workflows - Tight model-labeling feedback loops **Use external tools (CVAT, Label Studio, Labelbox) when:** - High-volume annotation with distributed teams - Complex labeling pipelines requiring external infrastructure FiftyOne integrates with external annotation tools. See [Integrations](../../integrations/index.md#integrations) for details. ## What’s Next - **Apply to your data** - Use this workflow on your production datasets - **Explore 3D fully** - Try more complex 3D scenes with occlusions - **Scale with teams** - The schema and QA workflow supports multiple annotators - **Explore plugins** - Check `@voxel51/brain` for advanced selection operators ## Resources * [FiftyOne 3D Visualizer](../../user_guide/app.md#app-3d-visualizer) * FiftyOne Brain - Embeddings * [FiftyOne Evaluation API](../../user_guide/evaluation/index.md) * [Annotation Integrations](../../integrations/index.md) * [Grouped Datasets](../../user_guide/groups.md) * [ZCore Repository](https://github.com/voxel51/zcore) * [YOLOv8 Documentation](https://docs.ultralytics.com/) ## Feedback Questions or suggestions? Reach us at `support@voxel51.com` or join our [Discord](https://community.voxel51.com).