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name: model-conversion description: "Routes Darknet weights, TensorFlow SavedModel, TFLite, quantization, and TensorRT conversion workflows for tensorflow-yolov4-tflite." disable-model-invocation: true metadata: disco-role: operating license: MIT

Model Conversion

Use this sub-skill when the user needs to convert YOLOv3/YOLOv4 or tiny Darknet .weights files into TensorFlow SavedModel, TFLite, float16/int8 TFLite, or TF-TRT artifacts with this repository's scripts.

Before conversion

• Read the root ../../references/compatibility.md for TensorFlow 2.3, protobuf, OpenCV, CPU/GPU, and TensorRT compatibility.
• Ensure the target checkout root is the command working directory. The repo's config reads relative paths such as ./data/classes/coco.names at import time.
• Confirm weights provenance and model family: YOLOv4 weights require --model yolov4; YOLOv3 weights require --model yolov3; tiny variants also require --tiny.
• Decide whether the downstream artifact is ordinary TensorFlow inference, TFLite/mobile inference, or TF-TRT. The save_model.py --framework value changes output tensors and post-processing expectations.

Main routes

1. Darknet weights to SavedModel: read references/workflows.md and generate the command with scripts/plan_conversion.py.
2. SavedModel to TFLite: read references/workflows.md for float32, float16, and int8 quantization requirements.
3. TensorRT / TF-TRT export: read references/workflows.md and references/troubleshooting.md.
4. Artifact sanity checks: use the expected outputs in references/cli-reference.md before handing an artifact to the inference sub-skill.

Command planner

Run the bundled planner to build safe commands without launching conversion:

python sub-skills/model-conversion/scripts/plan_conversion.py \
  --task full-tflite \
  --weights data/yolov4.weights \
  --saved-model checkpoints/yolov4-416 \
  --output checkpoints/yolov4-416.tflite \
  --model yolov4 --input-size 416

Use --check-paths only in a real target checkout when you want the planner to fail early on missing source weights or representative datasets.

Handoff to other sub-skills

• After a SavedModel or TFLite model exists, use ../inference-evaluation/SKILL.md to run image/video inference, mAP output generation, or FPS checks.
• If conversion needs a custom dataset or class file, use ../training-data/SKILL.md before changing class counts, anchors, or annotation paths.
• For mobile deployment, use ../android-deployment/SKILL.md after a compatible .tflite artifact is produced.

Stop conditions

Stop and ask the user before downloading large weights, creating a full COCO representative dataset, installing CUDA/TensorRT, or overwriting existing model artifacts. Those actions are outside safe planning and can be expensive or hardware-specific.