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name: data-and-training description: "Route U-2-Net dataset validation, transform inspection, and safe retraining preparation without starting long training by default." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data and Training

Use this sub-skill when the task is about U-2-Net training data layout, preprocessing transforms, batch/sample fields, the multi-side BCE training loop, or preparing a safe retraining run.

Start here

1. Confirm the user has a DUTS-style training root or a deliberately adapted equivalent.
2. Validate image/mask stem pairing with scripts/validate_training_layout.py before discussing a training launch.
3. Inspect one representative image and optional label with scripts/inspect_data_pipeline.py to confirm RescaleT/ToTensorLab-like output shapes and ranges.
4. Use training workflow for the exact source training settings and safe adaptation checklist.

Do not start the full training loop by default. The source loop is configured for epoch_num=100000 and data-dependent long execution; require explicit user approval and a bounded run plan first.

Core facts

• The training dataset is expected under train_data/DUTS/DUTS-TR/DUTS-TR/im_aug/*.jpg with matching masks under train_data/DUTS/DUTS-TR/DUTS-TR/gt_aug/*.png by the same filename stem.
• SalObjDataset yields sample dictionaries with imidx, image, and label keys; training composes RescaleT(320), RandomCrop(288), and ToTensorLab(flag=0).
• The source training loop defaults to model_name='u2net', batch_size_train=12, Adam learning rate 0.001, checkpoint save frequency 2000 iterations, and summed BCE losses across seven side outputs.
• model_name can be changed to u2netp; keep checkpoint paths and model constructor consistent with that choice.

References

• Data formats: dataset layout, sample dict schema, and transform behavior.
• Training workflow: training hyperparameters, loss setup, checkpoint behavior, and safe retraining preparation.
• API reference: distilled dataset/transform APIs and bundled script CLI contracts.
• Troubleshooting: missing data, mismatched stems, crop size, mask shape, memory, and checkpoint failures.

Routing boundaries

• Route salient-object or human segmentation inference to salient-object-inference.
• Route portrait inference, face crops, and portrait compositing to portrait-workflows.
• Route U2NET/U2NETP architecture internals and checkpoint shape debugging to model-architecture.
• Keep PaddleHub/Gradio demos out of this sub-skill unless the user is explicitly asking about optional demos rather than retraining.