---

name: inference description: "Guides Darkflow image, video, JSON, Python API, and protobuf inference/export workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Darkflow Inference and Export

Use this sub-skill when the user wants to run a trained Darkflow model, return predictions, save annotated images or JSON, demo a camera/video stream, or export/load a frozen graph.

Inputs you should collect

• A Darkflow-compatible .cfg model file or a .pb / .meta pair.
• A .weights file, checkpoint step, or existing .pb / .meta pair.
• Image folder, video file, or camera demo source.
• Optional labels file for custom, non-VOC, non-COCO model names.
• Optional threshold, GPU fraction, batch size, JSON-output, or save-video choices.

First checks

1. If the environment is not already proven, run ../../scripts/check_install.py.
2. Use ../../references/model-overview.md to select a model family and label source.
3. Use ../../references/cli-reference.md when building a flow command.
4. Use references/troubleshooting.md before retrying failed inference or export runs.

Route by task

• Image folder inference: Use references/workflows.md#image-folder-prediction.
• JSON output: Use references/workflows.md#json-output.
• Python API predictions: Use references/workflows.md#python-api-prediction and ../../references/api-reference.md.
• Camera or video demo: Use references/workflows.md#camera-or-video-demo.
• Frozen graph export / load: Use references/workflows.md#protobuf-export-and-load.

Outputs to expect

• Annotated images or JSON files are written under the selected image directory's out/ folder.
• return_predict() returns a list of dictionaries with label, confidence, topleft, and bottomright.
• --savepb writes a .pb graph and a .meta JSON metadata file.
• --saveVideo writes video.avi from demo mode.

Boundaries

This sub-skill does not own dataset annotation cleanup, custom-class config editing, or checkpoint training loops. Route those to ../training/SKILL.md, then return here for prediction or export after a trained model exists.

Quality checks before claiming success

• The CLI command or Python options include a valid model source and a valid weight/checkpoint/graph source.
• If JSON output is requested, the user knows to inspect the image directory's out/ folder.
• If using return_predict(), the input is already a numpy.ndarray image array.
• If using GPU flags, the user has a GPU-enabled TensorFlow 1.x stack; otherwise prefer CPU mode.