---

name: training description: "Guides Darkflow custom dataset preparation, YOLO config edits, training, and checkpoint workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Darkflow Training and Custom Data

Use this sub-skill when the user wants to train or fine-tune Darkflow on a custom object-detection dataset, prepare Pascal VOC annotations, edit label/config files, or resume checkpoints.

Inputs you should collect

• Image directory for --dataset.
• Pascal VOC XML annotation directory for --annotation.
• Label list: one class per line.
• A Darkflow-compatible .cfg model file.
• Optional pretrained .weights file or checkpoint step.
• Training controls such as --trainer, --lr, --batch, --epoch, --save, --summary, and --keep.

First checks

1. If the package is not already proven, run ../../scripts/check_install.py.
2. Validate the annotations and labels with scripts/check_voc_dataset.py before running a long training job.
3. Read references/data-formats.md when a dataset or label file is ambiguous.
4. Read ../../references/model-overview.md before changing class counts or filters.
5. Read references/troubleshooting.md before retrying a failed training run.

Route by task

• Custom dataset setup: Use references/data-formats.md plus scripts/check_voc_dataset.py.
• Config and label edits: Use references/workflows.md#custom-class-configuration and ../../references/model-overview.md.
• Training from weights: Use references/workflows.md#fine-tune-from-pretrained-weights.
• Training from scratch: Use references/workflows.md#train-from-scratch.
• Checkpoint resume or export handoff: Use references/workflows.md#checkpointing-and-resume, then hand off to ../inference/SKILL.md for export or prediction.

Outputs to expect

• Checkpoints under the selected backup directory, defaulting to ckpt/.
• Profile files saved beside checkpoints.
• Optional TensorBoard summaries under the selected summary directory.
• A trained model that can be loaded with --load <step> or --load -1 for the latest checkpoint.

Boundaries

This sub-skill does not own image-folder prediction, video/camera demos, JSON output, or protobuf load/export details. Those are handled by ../inference/SKILL.md after the model has weights or checkpoints.

Quality checks before claiming success

• The label list, final [region] classes, and penultimate [convolutional] filters agree.
• The annotation XML folder exists and each object label is in the label file.
• The image filenames referenced by annotation XMLs exist when an image directory is provided.
• The checkpoint path and --load value match the selected model name.
• The workflow does not assume external weights or datasets are downloadable unless the user explicitly allows network access.