Installs into .claude/skills of the current project.
Are you the author of Android Deployment?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/vectorspacelab-android-deployment)
---
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](../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](references/android-app.md) for Gradle,
asset, classifier, and delegate facts distilled from the app source.
- Use [scripts/check_android_assets.py](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
```bash
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](../model-conversion/SKILL.md).
- If Android detections disagree with Python results, compare against
[../inference-evaluation/SKILL.md](../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](../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.