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name: models-and-architectures description: "Select and reason about MedicalDetectionToolkit detector families, FPN/backbone settings, anchors, heads, losses, tensor shapes, and model result contracts while separating legacy custom-operator limits from portable source facts." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Models and architectures

Use this route when choosing mrcnn, ufrcnn, retina_net, retina_unet, or detection_unet, comparing 2D and 3D settings, interpreting model outputs, or diagnosing shape/anchor/loss problems. Begin with the experiment configuration route when cf does not yet exist, and use the CUDA route before exact MRCNN/U-FRCNN/Retina execution.

Workflow

1. Freeze cf.model, cf.dim, input channels, patch size, class count, and backbone/FPN stride assumptions. Read model configurations.
2. Choose a family using model overview, then check the corresponding result dictionary and loss/target contract.
3. Validate anchors, box coordinates, padding, and Dice/segmentation helpers with model utilities before changing a network implementation.
4. If import reaches NMS or RoIAlign, stop and run the read-only diagnostic in cuda-extensions. A current PyTorch CUDA import is not proof of this repository's historical detector path.
5. Record exact tensor shapes and whether a claim is source-backed, portable helper behavior, or unverified legacy runtime behavior.

Model-family routing

• Use Detection U-Net when the task is segmentation-first detection using connected components and configurable aggregation; it is the least coupled model path for source/API inspection.
• Use Mask R-CNN/U-FRCNN for two-stage proposal/classification/mask flows; they require RPN, RoIAlign, and legacy custom NMS/RoIAlign in the supplied implementation.
• Use RetinaNet/Retina U-Net for one-stage anchor classification and box regression; Retina U-Net additionally exposes the segmentation decoder and stride-1 path.
• dim=2 and dim=3 are not interchangeable: patch axes, pyramid strides, anchor scales, pooling/mask shapes, output box coordinate length, and memory use all change. Do not infer 3D runtime support from imports alone.

Acceptance boundary

The source modules, configuration defaults, and pure utility functions are usable evidence for a versioned operating graph. Exact detector construction, training, and inference are not verified on a modern stack when the custom extension import requires removed torch.utils.ffi; see troubleshooting. Do not implement a replacement model in this skill and do not substitute a CPU forward for the old CUDA-backed path.

Read the linked references for detailed family comparisons, configuration fields, tensor/result contracts, utility signatures, and failure recovery.