---

name: darkflow description: "Routes Darkflow object-detection, training, and export workflows for the legacy YOLO package." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Darkflow

Darkflow is the legacy TensorFlow 1.x YOLO package exposed by the flow CLI and the darkflow.net.build.TFNet API.

Use this skill when the user wants to:

• run detections on images, folders, videos, or a webcam
• read JSON predictions or call return_predict() from Python
• export a frozen .pb / .meta pair or load one for inference
• train or fine-tune a YOLO model on Pascal VOC style annotations
• adapt Darkflow-compatible config files and labels for a custom dataset

Start here

1. If the package is not installed yet, read references/installation.md.
2. Run scripts/check_install.py to confirm the import stack before deeper work.
3. Use sub-skills/inference/SKILL.md for prediction, demo, JSON, and protobuf export/import.
4. Use sub-skills/training/SKILL.md for dataset prep, labels, training, and checkpoints.

Verified install shape

The verified inspection path used a Python 3.6 environment with:

• tensorflow==1.4.1
• opencv-python
• numpy
• requests
• Cython<3
• pytest and pytest-cov for test-backed validation

If editable install fails with a Cython compile error, see references/troubleshooting.md. The legacy build needs a pre-3.0 Cython release.

Quick smoke checks

• python scripts/check_install.py
• flow --help or python scripts/flow.py --help
• python -m pip check

Read these references when needed

• references/cli-reference.md for flags, defaults, and command patterns
• references/api-reference.md for TFNet and argHandler signatures
• references/model-overview.md for bundled config families and label rules
• references/troubleshooting.md for install, import, build, and runtime failures
• references/repo-provenance.md before refreshing or comparing this skill to a new checkout

Route map

• Inference / export: image folders, JSON output, camera or video demo, Python predictions, .pb export, or .pb / .meta loading.
• Training / custom data: --train, Pascal VOC XML annotations, labels.txt, --dataset, --annotation, checkpointing, or custom config editing.

If a request spans both routes, begin with training for dataset/config preparation and finish with inference for prediction or export.