fiftyone-dataset-import
Universal Dataset Import for FiftyOne
Key Directives
ALWAYS follow these rules:
1. Scan folder FIRST
Before any import, deeply scan the directory to understand its structure:
# Use bash to explore
find /path/to/data -type f | head -50
ls -la /path/to/data
2. Auto-detect everything
Detect media types, label formats, and grouping patterns automatically. Never ask the user to specify format if it can be inferred.
3. Detect multimodal groups
Look for patterns that indicate grouped data:
More from voxel51/fiftyone-skills
fiftyone-find-duplicates
Finds duplicate or near-duplicate images in FiftyOne datasets using brain similarity computation. Use when deduplicating datasets, finding similar images, or removing redundant samples.
20fiftyone-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.
12fiftyone-dataset-inference
Run ML model inference on FiftyOne datasets. Use when running models for detection, classification, segmentation, or embeddings. Discovers available models dynamically from the Zoo, plugin operators, or custom sources — never assumes a fixed model list.
12fiftyone-dataset-export
Exports 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.
11fiftyone-embeddings-visualization
Visualizes datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when exploring dataset structure, finding clusters, identifying outliers, or understanding data distribution.
11fiftyone-code-style
Writes Python code following FiftyOne's official conventions. Use when contributing to FiftyOne, developing plugins, or writing code that integrates with FiftyOne's codebase.
10