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name: android-deployment description: "Routes Android TFLite asset, Gradle, classifier, GPU delegate, and mobile YOLOv4 deployment workflows for tensorflow-yolov4-tflite." disable-model-invocation: true metadata: disco-role: operating license: MIT

Android Deployment

Use this sub-skill when the user wants to run or adapt the repository's Android TFLite demo, replace the .tflite asset, update labels, reason about the YoloV4Classifier constants, or troubleshoot Gradle/TFLite delegate issues.

Before Android work

• Produce and validate a TFLite model through ../model-conversion/SKILL.md before replacing Android assets.
• Verify the model with Python TFLite inference before debugging Android UI or camera code.
• Read references/android-app.md for Gradle, asset, classifier, and delegate facts distilled from the app source.
• Use scripts/check_android_assets.py to check model/label asset names and class-count consistency in a target app.

Main routes

1. Replace the bundled model: confirm the asset filename expected by MainActivity and DetectorActivity, copy the validated .tflite into the app assets directory of the user's target checkout, then run the asset checker.
2. Update labels/classes: replace coco.txt or change the Java constants so the classifier reads the intended label file; class order must match the TFLite model output.
3. Choose delegates: inspect YoloV4Classifier constants for GPU/NNAPI; GPU delegate is enabled by source default, NNAPI is disabled.
4. Build/run the app: verify Android SDK/Gradle compatibility and be aware that the source Gradle file applies a model-download task that fetches a default MobileNet zip unless changed.

Asset checker examples

python sub-skills/android-deployment/scripts/check_android_assets.py \
  --android-root android \
  --model yolov4-416-fp32.tflite \
  --labels coco.txt \
  --expected-classes 80

Use --assets-dir instead of --android-root if the user has copied the app layout into another project.

Handoff to other sub-skills

• If the .tflite model is missing or mismatched, return to ../model-conversion/SKILL.md.
• If Android detections disagree with Python results, compare against ../inference-evaluation/SKILL.md using the same input image, model file, class labels, input size, and thresholds.
• If class IDs are out of range, use ../training-data/SKILL.md to validate the class file and data/model class count.

Stop conditions

Stop before installing Android SDKs, downloading Gradle dependencies, running a camera app on a device, or replacing production mobile assets unless the user approves the target project and overwrite policy.