Viewer

Inspect a finished dataset in FiftyOne with the bundled script:

python examples/view_dataset.py

Set DATASET_DIR (and optionally DATASET_NAME) at the top of the script first. The script detects the layout, imports the dataset, prints per-split sample counts and a class-distribution table, then opens the FiftyOne app. FiftyOne is a project dependency, so no extra install is required.

Supported Layouts

Layout	Detected by	DATASET_DIR should be
merged	merged_coco_annotations.json at the root	The dataset root produced by merge_shards().
shard	coco_annotations.json plus images/	A single shard_* directory.
coco	_annotations.coco.json at the root or in train/valid/test	The DatasetConverter COCO output root, or one split.
yolo	data.yaml at the root	The DatasetConverter YOLO output root.

Detection precedence is merged, then shard, then yolo, then coco. The console table reports, per class, the count and share within each split, the total instances, the number of distinct images containing the class, and that class's share of the grand total. For interactive charts, add a Histograms panel next to the Samples tab in the app.

Quick previews

The lightweight tkinter viewer used by the quickstart examples is a preview tool only. Use FiftyOne for final dataset inspection.

Next Steps

Generating a Dataset with a Worker

Run a spec-driven bin-picking dataset end to end: generate shards, merge them, and split into train, valid, and test.

Open tutorial →

Merging and Converting

Merge shards into one annotation file and split it into train, valid, and test in COCO or YOLO format.

Explore →