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name: inference-evaluation description: "Routes Mask_RCNN inference, visualization, color splash, COCO evaluation, and nucleus RLE submission tasks." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Inference, Visualization, and Evaluation

Use this sub-skill when a task asks how to run Mask_RCNN inference, inspect masks/boxes, produce color splash outputs, compute COCO metrics, or encode nucleus submissions.

Read first by need

• Read inference-workflows.md for MaskRCNN(mode="inference"), detect(), and postprocessing flow.
• Read evaluation-and-visualization.md for display helpers, COCO result formatting, AP, and nucleus RLE details.
• Read troubleshooting.md for empty predictions, image-shape mismatches, and encoding mistakes.
• Use scripts/apply_color_splash.py for a safe standalone splash postprocessor.
• Use scripts/rle_tools.py to inspect or convert nucleus-style RLE strings without the original notebook.

Inference workflow

1. Build an inference config that usually sets GPU_COUNT = 1, IMAGES_PER_GPU = 1, and DETECTION_MIN_CONFIDENCE = 0 for inspection work.
2. Load weights.
3. Call model.detect([image]) where the list length matches BATCH_SIZE.
4. Inspect the returned dictionary keys: rois, class_ids, scores, and masks.
5. Route visualization to mrcnn.visualize.display_instances or the bundled splash/RLE helpers.
6. For evaluation, use the dataset-specific converter or the COCO AP helpers in the references.

Boundary notes

• Dataset class loading and layout checks belong to data-preparation.
• Training schedules and weight selection belong to training.
• API signatures and graph-build compatibility belong to core-apis.