Detectron2.
Detectron2 is Meta's computer vision library for object detection and segmentation, implementing Mask R-CNN and related architectures with a modular design and a model zoo of pretrained weights.
Where Detectron2 fits on a long engagement.
Detectron2 is where to go when a bounding box is not enough. Instance segmentation produces a pixel-level mask per object, which is what medical imaging, precise measurement and any application computing area or shape actually requires.
It trades speed for accuracy. The two-stage architectures it implements are more accurate on difficult and overlapping objects than single-stage detectors, and correspondingly slower — which is the right trade for batch analysis and the wrong one for live video.
What an assigned team does with Detectron2.
Segmentation annotation is substantially more expensive than bounding boxes. Pixel-level masks take an order of magnitude longer per image, and that cost has to be in the project plan from the start rather than discovered during data preparation.
Being honest about that at scoping is part of how work is estimated, as set out in how the monthly fee is built.
What we use Detectron2 for.
- Pixel-level masks where boxes are insufficient Area and shape measured, which a bounding box cannot express.
- Accuracy on overlapping objects Two-stage detection where objects occlude each other and single-stage struggles.
- Annotation cost planned honestly Mask labelling scoped at its real cost rather than estimated like bounding boxes.
How Detectron2 capacity is assigned.
Segmentation work is assigned inside AI capacity, with annotation effort estimated realistically before the project is committed.
Tell us what your roadmap needs Detectron2 for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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