---

name: training-data description: "Use when preparing RobustVideoMatting datasets, DATA_PATHS, augmentations, losses, or four-stage training commands with GPU and data-layout caveats." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

RobustVideoMatting Training and Data

Use this sub-skill for training setup, dataset schemas, path validation, augmentation behavior, and adapting the official four-stage RVM training recipe.

Read this when

• The user asks how to train or fine-tune RVM.
• The task mentions train.py, train_config.py, DATA_PATHS, VideoMatte240K, ImageMatte, COCO panoptic, Supervisely Person Dataset, or YouTubeVIS.
• You need to validate dataset folders before editing a config.
• The user hits training dependency, dataloader, NCCL, CUDA, OOM, or path errors.

Route other tasks elsewhere:

• Model forward-call details: model-api.
• Inference/conversion: inference-workflows.
• Evaluation metrics and synthetic test composites: evaluation-tools.

Training setup workflow

1. Treat full RVM training as a large multi-GPU workflow, not a smoke test. The official training reference used data-center scale hardware and large external datasets.

2. Prepare dataset roots using the schemas in references/data-layouts.md. Edit the DATA_PATHS mapping in a local training checkout to point at those roots.

3. Validate obvious directory mistakes before launching training:

   python scripts/rvm_validate_data_layout.py \
     --videomatte-train /data/VideoMatte240K_JPEG_SD/train \
     --background-images-train /data/Backgrounds/train \
     --background-videos-train /data/BackgroundVideos/train \
     --strict
   

4. Choose the stage command from references/training-reference.md, then adjust paths, GPU count, --num-workers, batch size, checkpoint locations, and logging directories for the target machine.

5. Validate failures against references/troubleshooting.md before retrying a long run.

Bundled references and script

• Read references/data-layouts.md for required directory shapes and DATA_PATHS keys.
• Read references/training-reference.md for training flags, four official stages, distributed behavior, losses, and scale warnings.
• Read references/dataset-api.md for dataset and augmentation class behavior, sample outputs, and tensor shapes.
• Read references/troubleshooting.md for missing data, relative paths, dependency pins, dataloader workers, CUDA/NCCL, and OOM recovery.
• Run scripts/rvm_validate_data_layout.py for safe filesystem validation. It does not import torch or start training.

Key decisions

• Use mobilenetv3 for the documented official stage examples unless the user asks to train the ResNet50 variant.
• Do not start train.py on CPU as a validation shortcut. The script discovers torch.cuda.device_count() and uses multiprocessing, NCCL, DDP, SyncBatchNorm, and AMP-oriented logic.
• Reduce --num-workers first when dataloaders exit unexpectedly on machines with limited CPU memory.
• Keep exact legacy requirement pins as historical repo evidence. For modern inspection or helper execution, use compatible PyTorch/TorchVision versions, but do not claim that full legacy training was verified unless it was.

Acceptance check for training/data answers

A good answer names the dataset roots and expected subdirectories, distinguishes matting and segmentation datasets, gives the stage command or config change, states GPU/data scale limitations, recommends a safe layout validation step, and avoids presenting full training as a quick smoke test.