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name: training description: "Guides Mask_RCNN training, fine-tuning, weights, checkpoints, layer selection, schedules, callbacks, and backend constraints." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Training and Fine-tuning

Use this sub-skill when a task asks to train, fine-tune, resume, configure, or troubleshoot Matterport Mask_RCNN training on COCO, Balloon/VIA, Nucleus, Shapes, or a custom instance segmentation dataset.

Read first by need

• Read training-workflows.md for the API training sequence, sample schedules, layer selections, and augmentation notes.
• Read weights-and-checkpoints.md for COCO/ImageNet/last/custom weight loading and checkpoint behavior.
• Read troubleshooting.md for class-head mismatch, batch-size, memory, empty masks, and slow training issues.
• Use scripts/plan_training.py to generate a safe training plan summary without running training.
• Adapt scripts/minimal_training_template.py when writing a project-specific training script that does not depend on original sample files.

Training workflow

1. Prepare training and validation datasets with data-preparation. Both datasets must be loaded and prepare() must be called.

2. Define a training Config with the correct NUM_CLASSES, image sizes, STEPS_PER_EPOCH, VALIDATION_STEPS, and memory-aware GPU_COUNT/IMAGES_PER_GPU.

3. Build the model:

   from mrcnn import model as modellib
   model = modellib.MaskRCNN(mode="training", config=config, model_dir="logs")
   

4. Load initial weights. For COCO-to-custom transfer, exclude class-specific heads.

5. Train heads first, then optionally deeper ResNet/FPN layers:

   model.train(dataset_train, dataset_val,
               learning_rate=config.LEARNING_RATE,
               epochs=30,
               layers="heads")
   model.train(dataset_train, dataset_val,
               learning_rate=config.LEARNING_RATE / 10,
               epochs=60,
               layers="all")
   

6. Validate by building an inference config and routing detection/evaluation to inference-evaluation.

Decision points

• New class count? Set NUM_CLASSES = 1 + foreground_count and exclude final heads when loading COCO weights.
• Small dataset? Start with layers="heads"; use augmentation carefully; freeze batch norm unless you have large batches.
• CPU-only? Treat training as a code/data smoke test, not a practical run.
• Multi-GPU? Verify exact TensorFlow/CUDA/Keras compatibility before using GPU_COUNT > 1.
• No original sample script? Use the bundled training template and references; this skill is self-contained.