claude

fiftyone-model-evaluation

Evaluate model predictions against ground truth using COCO, Open Images, or custom protocols. Use when computing mAP, precision, recall, confusion matrices, or analyzing TP/FP/FN examples for detection, classification, segmentation, or regression tasks.

48 on GitHubApache-2.0updated 14 days agoEvaluation
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Plugin fiftyone-model-evaluation from marketplace fiftyone-skills in voxel51/fiftyone-skills. Commands read from the repo's manifest — 12 days ago.

What it installs

Skills (1)

  • fiftyone-model-evaluation

    Evaluate model predictions against ground truth using COCO, Open Images, or custom protocols. Use when computing mAP, precision, recall, confusion matrices, or analyzing TP/FP/FN examples for detection, classification, segmentation, or regression tasks.

View source on GitHub

More from voxel51/fiftyone-skills

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Build high-quality datasets and computer vision models. Visualize datasets, analyze models, find duplicates, run inference, evaluate predictions, and develop custom plugins.

19 skills · 2 commands · 1 MCP server

48

Drive the FiftyOne App via the Playwright MCP for operator/plugin verification, demo or screencast recording, and end-to-end UI automation against `fo.launch_app(...)`. Use when automating the FiftyOne App, recording an operator demo, or debugging a session that silently dies after a UI action (the `browser_navigate`-after-`reload_dataset` crash), covering launcher patterns (`remote=True`, trigger-file IPC), MUI input/dropdown gotchas, `data-cy` selectors, and scoped cleanup.

1 skill

48

Write Python code following FiftyOne's official conventions. Use when contributing to FiftyOne, developing plugins, or writing code that integrates with FiftyOne's codebase. Covers module structure, import organization, Google-style docstrings, lazy imports, guard patterns, and error handling.

1 skill

48

Creates Jupyter notebooks for FiftyOne workflows including getting-started guides, tutorials, recipes, and full ML pipelines. Use when users want to create a notebook, write a tutorial, build a demo, document a workflow, or generate a FiftyOne walkthrough covering data loading, exploration, inference, evaluation, and export. Builds notebooks cell-by-cell using NotebookEdit.

1 skill

48

End-to-end dataset curation for any FiftyOne dataset: inspect schema and media quality (blur, brightness, corruption, resolution), audit annotation quality (mistakenness, hardness, duplicate labels, IoU overlaps), analyze class distributions and imbalances, explore embedding space and data gaps, find near/exact duplicates, create curated subsets using semantic search and brain ops, build train/val/test splits, and answer natural language questions about your data. Delegates to fiftyone-find-duplicates and fiftyone-embeddings-visualization. Requires FiftyOne MCP server with @voxel51/brain and @voxel51/utils plugins.

1 skill

48

Export FiftyOne datasets to standard formats (COCO, YOLO, VOC, CVAT, CSV, etc.) and Hugging Face Hub. Use when converting datasets, exporting for training, creating archives, sharing data in specific formats, or publishing datasets to Hugging Face Hub.

1 skill

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