fiftyone-dataset-inferencelisted

Create a FiftyOne dataset from a directory of media files (images, videos, point clouds), optionally import labels in common formats (COCO, YOLO, VOC), run model inference, and store predictions. Use when users want to load local files into FiftyOne, apply ML models for detection, classification, or segmentation, or build end-to-end inference pipelines.
aiskillstore/marketplace · ★ 329 · Data & Documents · score 79

Install: claude install-skill aiskillstore/marketplace

# Create Dataset and Run Inference ## Overview Create FiftyOne datasets from local directories, import labels in standard formats, and run model inference to generate predictions. **Use this skill when:** - Loading images, videos, or point clouds from a directory - Importing labeled datasets (COCO, YOLO, VOC, CVAT, etc.) - Running model inference on media files - Building end-to-end ML pipelines ## Prerequisites - FiftyOne MCP server installed and running - `@voxel51/io` plugin for importing data - `@voxel51/zoo` plugin for model inference - `@voxel51/utils` plugin for dataset management ## Key Directives **ALWAYS follow these rules:** ### 1. Explore directory first Scan the user's directory before importing to detect media types and label formats. ### 2. Confirm with user Present findings and get confirmation before creating datasets or running inference. ### 3. Set context before operations ```python set_context(dataset_name="my-dataset") ``` ### 4. Launch App for inference ```python launch_app(dataset_name="my-dataset") ``` ### 5. User specifies field names Always ask the user for: - Dataset name - Label field for predictions ### 6. Close app when done ```python close_app() ``` ## Workflow ### Step 1: Explore the Directory Use Bash to scan the user's directory: ```bash ls -la /path/to/directory find /path/to/directory -type f | head -20 ``` Identify media files and label files. See **Supported Dataset Types** section for format detection. ### Step 2: Pre