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name: conversion description: "Guides TensorFlow YOLOv3 checkpoint conversion, COCO initialization, Darknet weight pitfalls, and graph freezing to PB inference models." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

TensorFlow YOLOv3 conversion operating guide

Use this sub-skill when a user asks to convert pretrained YOLOv3 weights, initialize training from COCO, freeze a TensorFlow checkpoint, diagnose convert_weight.py / freeze_graph.py, or decide whether the bundled Darknet conversion scripts are safe to run.

Boundaries

• Owns checkpoint and weight conversion plus freezing a TensorFlow 1.x graph to a .pb file.
• Routes running image/video inference with the final .pb to the inference sub-skill.
• Routes training after COCO initialization to the training sub-skill.
• Routes dataset, class-name, and anchor-file preparation to the data-preparation sub-skill.
• Does not claim that direct Darknet .weights conversion works unmodified; the bundled direct-conversion scripts have known source bugs.

Required working assumptions

• Run repository scripts from the repository root so relative config paths such as ./data/classes/coco.names, ./data/anchors/basline_anchors.txt, and ./checkpoint/... resolve correctly.
• Use a TensorFlow 1.x-compatible environment. TensorFlow 1.15.x CPU is sufficient for conversion/freezing if the checkpoint and graph are compatible; legacy tensorflow-gpu==1.11.0 is optional and difficult on modern CUDA hardware.
• Default conversion inputs and outputs are:
  • original release checkpoint prefix: ./checkpoint/yolov3_coco.ckpt (cfg.YOLO.ORIGINAL_WEIGHT)
  • converted/demo checkpoint prefix: ./checkpoint/yolov3_coco_demo.ckpt (cfg.YOLO.DEMO_WEIGHT)
  • frozen graph output: ./yolov3_coco.pb
  • freeze output nodes: input/input_data, pred_sbbox/concat_2, pred_mbbox/concat_2, pred_lbbox/concat_2

Fast operating path

1. Read conversion workflows for the exact COCO release checkpoint, --train_from_coco, and freeze flows.

2. Run the safe preflight checker before expensive or destructive commands:

   python sub-skills/conversion/scripts/check_conversion_inputs.py --repo-root .
   

3. If creating custom-class training initialization from COCO, update the class-name config first, then use:

   python convert_weight.py --train_from_coco
   

   This intentionally skips the output head variables (conv_sbbox, conv_mbbox, conv_lbbox) and saves randomly initialized heads into cfg.YOLO.DEMO_WEIGHT.

4. If creating a COCO demo PB, use the release checkpoint flow, run python convert_weight.py, then run python freeze_graph.py.

5. If the user supplies Darknet .weights, consult conversion troubleshooting before running anything; prefer the release checkpoint flow or patch a copy of the direct conversion scripts first.

Safety checklist

• Confirm checkpoint prefixes have .meta, .index, and at least one .data-* shard before restore.
• Confirm class-name and anchor files are present and match the graph/class-count intent.
• For freeze_graph.py, use output node names without :0; inference code fetches the same tensors with :0 suffix.
• Keep generated checkpoints and PB files in user-selected working paths; do not copy large model artifacts into this skill directory.