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name: inference-evaluation description: "Routes image/video detection, TFLite inference, mAP evaluation, and FPS benchmarking workflows for tensorflow-yolov4-tflite." disable-model-invocation: true metadata: disco-role: operating license: MIT

Inference and Evaluation

Use this sub-skill when the user has a TensorFlow SavedModel, TFLite model, or TF-TRT SavedModel from this repository and wants to run image detection, video detection, COCO mAP output generation, or FPS benchmarking.

Before running inference

• If the model artifact does not exist yet, route to ../model-conversion/SKILL.md.
• Confirm that --framework, --model, --tiny, and --size match the artifact produced during conversion.
• Run commands from the target checkout root because the repo uses relative config/data paths at import time.
• Treat image/video/model/dataset paths as user-provided runtime inputs. Do not download weights or COCO data unless the user explicitly approves.

Main routes

1. Image detection: read references/workflows.md and use scripts/plan_inference.py to build a checked detect.py command.
2. Video detection: read references/workflows.md before using detectvideo.py, especially on headless machines.
3. TFLite inference: read references/workflows.md for output order and model/tiny caveats.
4. mAP evaluation: read references/workflows.md because the source script deletes/recreates output directories and has a config-path trap.
5. FPS benchmarking: read references/workflows.md and verify that the user's backend claim matches the actual environment.

Command planner

Example image-detection plan:

python sub-skills/inference-evaluation/scripts/plan_inference.py \
  --action detect-image \
  --framework tf \
  --weights checkpoints/yolov4-416 \
  --input data/kite.jpg \
  --output result.png \
  --model yolov4 --size 416

Example TFLite plan:

python sub-skills/inference-evaluation/scripts/plan_inference.py \
  --action detect-image \
  --framework tflite \
  --weights checkpoints/yolov4-416.tflite \
  --input data/kite.jpg \
  --output result-tflite.png \
  --model yolov4 --size 416

Add --check-paths when operating inside a real target checkout and you want missing model/input/annotation files to fail before launching TensorFlow.

Output checks

• Image detection should write the --output image and draw boxes through core.utils.draw_bbox.
• Video detection writes --output only when that flag is supplied; otherwise it opens an OpenCV UI window unless disabled.
• Evaluation writes text files under mAP/predicted and mAP/ground-truth, then the mAP tool reads those directories.
• Benchmarking prints per-iteration time, average FPS, and instantaneous FPS; discard the first warmup iteration when interpreting results.

Troubleshooting

Use references/troubleshooting.md for missing model files, blank OpenCV inputs, TFLite output-order confusion, mAP directory side effects, annotation path mismatch, GPU/TensorRT benchmark claims, and headless display failures.