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name: data-preparation description: "Guides Mask_RCNN dataset subclassing, mask formats, sample dataset layouts, validation, resizing, and synthetic fixtures." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Data Preparation

Use this sub-skill when a task asks how to prepare images, masks, polygons, COCO annotations, VIA JSON, nucleus challenge folders, or custom mrcnn.utils.Dataset subclasses for Mask_RCNN.

Read first by need

• Read dataset-contract.md for the Dataset subclass lifecycle and mask/image requirements.
• Read data-formats.md for Shapes, Balloon/VIA, COCO, and Nucleus layouts.
• Read troubleshooting.md when masks are empty, classes mismatch, polygons fail, or image sizes are wrong.
• Run scripts/validate_dataset_layout.py to check known sample layout conventions without importing Mask_RCNN.
• Run scripts/generate_shapes_fixture.py to create a tiny synthetic fixture for smoke tests or documentation examples.

Dataset preparation workflow

1. Subclass mrcnn.utils.Dataset.

2. In a loader method, call add_class(source, class_id, class_name) and add_image(source, image_id, path, **metadata) for each image.

3. Implement load_mask(image_id) to return:

   mask: np.ndarray  # shape [height, width, instance_count], bool or 0/1
   class_ids: np.ndarray  # shape [instance_count], dtype int32
   

4. Override load_image() only when images are generated or not stored as ordinary files.

5. Call dataset.prepare() before using dataset.image_ids, class_names, source_class_ids, load_image_gt, training, or visualization.

6. Use mrcnn.model.load_image_gt(dataset, config, image_id, augmentation=..., use_mini_mask=...) to check the full preprocessing path.

Data-specific routes

• VIA polygons / Balloon-style one-class dataset: validate folder split and via_region_data.json; read data-formats.md.
• COCO: validate annotations/instances_<subset><year>.json plus <subset><year>/ images; pycocotools is required for actual loading and evaluation.
• Nucleus / Data Science Bowl: validate <image_id>/images/<image_id>.png and optional <image_id>/masks/*.png; RLE output is owned by inference-evaluation.
• Synthetic Shapes: use generated shapes for quick data-pipeline checks; do not treat them as proof of real dataset quality.

Boundary notes

This sub-skill validates and explains data. For training schedules, layer choices, checkpoints, and weight loading, route to training. For prediction outputs, COCO AP, visualization, color splash, or RLE submission files, route to inference-evaluation.