fiftyone-embeddings-visualizationlisted

# Embeddings Visualization in FiftyOne ## Overview Visualize your dataset in 2D using deep learning embeddings and dimensionality reduction (UMAP/t-SNE). Explore clusters, find outliers, and color samples by any field. **Use this skill when:** - Visualizing dataset structure in 2D - Finding natural clusters in images - Identifying outliers or anomalies - Exploring data distribution by class or metadata - Understanding embedding space relationships ## Prerequisites - FiftyOne MCP server installed and running - `@voxel51/brain` plugin installed and enabled - Dataset with image samples loaded in FiftyOne ## Key Directives **ALWAYS follow these rules:** ### 1. Set context first ```python set_context(dataset_name="my-dataset") ``` ### 2. Launch FiftyOne App Brain operators are delegated and require the app: ```python launch_app() ``` Wait 5-10 seconds for initialization. ### 3. Discover operators dynamically ```python # List all brain operators list_operators(builtin_only=False) # Get schema for specific operator get_operator_schema(operator_uri="@voxel51/brain/compute_visualization") ``` ### 4. Compute embeddings before visualization Embeddings are required for dimensionality reduction: ```python execute_operator( operator_uri="@voxel51/brain/compute_similarity", params={ "brain_key": "img_sim", "model": "clip-vit-base32-torch", "embeddings": "clip_embeddings", "backend": "sklearn", "metric": "cosine" } ) ``` ### 5.