Most CV projects spend more time on data plumbing, visualization and format conversions than on model code. This library focuses on that gap: compact, model-agnostic utilities that make it trivial to load common datasets, draw and compose detection/segmentation visualizations, convert between annotation formats, and plug into existing inference libraries.
What Sets It Apart
- Model-agnostic annotators and visualizers: a composable set of annotators (boxes, masks, labels, counts, heatmaps) so you can build clear diagnostic visualizations across different model outputs. This means fewer custom plotting scripts and faster iteration when inspecting results.
- Practical dataset utilities: loaders and converters for COCO/YOLO/Pascal VOC with lazy image loading and easy merging/splitting. So you can standardize heterogeneous datasets for training or evaluation without writing conversion glue.
- Connectors to inference stacks: out-of-the-box adapters for popular model ecosystems (Ultralytics, Transformers, MMDetection, Roboflow Inference), letting you convert raw model outputs into a unified Detections format for downstream tooling.
Who It's For & Trade-offs
Great fit if you: need repeatable, readable visualizations for model debugging; are preparing or converting datasets between common CV formats; or want a lightweight layer to standardize model outputs for analytics and demos. Look elsewhere if you: need a full training framework or production model-serving stack—this library complements training libraries (e.g., MMDetection, Detectron2) rather than replacing them. It also prioritizes clarity and interoperability over offering low-level, high-performance inference primitives.
Where It Fits
Think of it as the "glue and visualization" layer in a CV pipeline: between dataset storage/annotation and model training/inference. Use it for exploratory data analysis, annotation tooling, demo rendering, and to normalize outputs from varied model providers for downstream evaluation.
