CLI tool for working with vision AI datasets
Project description
argus-cv
Vision AI dataset toolkit for working with YOLO, COCO, and semantic mask datasets.
Library users can start with the Python API reference:
Installation
uvx argus-cv
Highlights
- Detects YOLO, COCO, and folder-based semantic mask datasets.
- Reports per-class stats (pixel coverage for mask datasets).
- Interactive viewer with bounding boxes, polygons, or mask overlays.
- Split and unsplit YOLO/COCO/mask datasets.
Usage
# List datasets in current directory
uvx argus-cv list
# List datasets in specific path
uvx argus-cv list --path /path/to/datasets
# Limit search depth
uvx argus-cv list --path . --max-depth 2
# Show instance statistics for a dataset
uvx argus-cv stats /path/to/dataset
# View annotations interactively
uvx argus-cv view /path/to/dataset --split val
# Split an unsplit dataset into train/val/test
uvx argus-cv split /path/to/dataset -o /path/to/output -r 0.8,0.1,0.1
# Merge a split dataset back into unsplit layout
uvx argus-cv unsplit -d /path/to/split_dataset -o /path/to/output
# Filter dataset to selected classes
uvx argus-cv filter /path/to/dataset -o /path/to/output --classes person,car
# Convert mask -> YOLO segmentation
uvx argus-cv convert -i /path/to/masks -o /path/to/output --to yolo-seg
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