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Phase 5: activate make/model identification + car-presence gate
Wires up the make/model identifier that was trained in Phase 1.5 but never called, and adds a "is there a car, and where?" gate in front of it — both on the ResNet-50 family (the gate is a COCO Mask R-CNN; masks ignored, boxes used). New modules (ccdp.identification): - car_gate.py — CarGate (COCO maskrcnn_resnet50_fpn) + pure decide()/pad_box; returns the highest-scoring car/truck/bus box or has_car=False. - ml_identifier.py — MLIdentifier inference wrapper around the production identifier checkpoint; maps class idx -> make/model/year via a bundled Stanford Cars class-name resource (no dataset needed). - auto_identify.py — orchestrates gate -> crop -> identifier into one call, returning an IdentificationResult the cost pipelines accept. - data/stanford_cars_classes.json — bundled 196-class name map. Integrations: - car_identifier.identify() gains an opt-in ML stage (use_ml=, injectable), ordered af…c9f7f1e