name: tensorflow-yolov4-tflite
description: "Guides TensorFlow YOLOv3/YOLOv4 conversion, inference, training
data, evaluation, TensorRT, and Android TFLite workflows for
hunglc007/tensorflow-yolov4-tflite."
disable-model-invocation: true
metadata:
disco-role: operating
license: MIT
tensorflow-yolov4-tflite
Use this repo skill when the user needs operational guidance for the
hunglc007/tensorflow-yolov4-tflite project: YOLOv3/YOLOv4 and tiny variants
implemented in TensorFlow 2, Darknet .weights conversion to SavedModel,
TFLite or TF-TRT export, image/video detection, COCO/VOC annotation preparation,
training, mAP/FPS checks, or the bundled Android TFLite demo.
First checks
- Confirm the user is working with a target checkout of this repository or a
project copied from it. The source scripts use repository-relative paths such
as
./data/classes/coco.names; run commands from the target checkout root or
pass absolute paths where a flag supports them.
- Read references/repo-provenance.md when
checking whether this skill matches a checkout before refreshing it.
- Read references/compatibility.md before
installing dependencies. The upstream pins are old: TensorFlow 2.3 era,
Python 3.8 is the safest reconstruction target, modern protobuf must often
be pinned below 3.20, and GPU/TensorRT needs a matching CUDA 10.x-era stack.
- Run scripts/check_environment.py against the
user's target checkout for a non-destructive import/config/backend probe.
Minimal environment probe after installation:
python - <<'PY'
import tensorflow as tf, cv2
from core.config import cfg
from core import utils
print("tensorflow", tf.__version__)
print("opencv", cv2.__version__)
print("class_file", cfg.YOLO.CLASSES)
print("classes", len(utils.read_class_names(cfg.YOLO.CLASSES)))
print("gpus", tf.config.experimental.list_physical_devices("GPU"))
PY
If this fails, go to references/troubleshooting.md
before changing the task plan.
Route by task
- Convert models or export formats: use
sub-skills/model-conversion/SKILL.md
for Darknet
.weights to TensorFlow SavedModel, TFLite float32/float16/int8,
TF-TRT conversion, supported flags, artifact checks, and conversion failures.
- Run detection, evaluation, or benchmarks: use
sub-skills/inference-evaluation/SKILL.md
for image/video inference, TFLite inference, mAP file generation, FPS tests,
thresholds, output files, and post-processing checks.
- Prepare data or train: use
sub-skills/training-data/SKILL.md for
COCO/VOC annotation formats, class/anchor config, dataset validation, training
stages, freeze layers, checkpoints, and dataset-related errors.
- Use the Android demo: use
sub-skills/android-deployment/SKILL.md
for TFLite asset placement, Gradle/TFLite versions, YOLOv4 classifier
constants, GPU/NNAPI delegate choices, and Android build/run troubleshooting.
Repo operating model
- This is a script-style repository, not an installable Python distribution.
Future agents should install the dependencies in an isolated environment and
add the target checkout root to
PYTHONPATH when running scripts from another
working directory.
core.config.cfg owns the default class file, anchors, strides, train/test
annotation paths, thresholds, image size, batch size, and epoch counts.- The main Python entry points are
save_model.py, convert_tflite.py,
convert_trt.py, detect.py, detectvideo.py, evaluate.py,
benchmarks.py, and train.py. The sub-skills distill their flags and safe
command-building steps; do not assume a command is safe to run until weights,
datasets, output paths, and backend requirements are checked.
- Full YOLOv4 weights, COCO datasets, TensorRT conversion, Android builds, and
training are network-, data-, hardware-, or time-intensive. Treat them as
explicit user-approved actions, not default verification steps.
Bundled helpers
Do not use this skill when
- The user is asking about a different YOLO implementation such as Darknet,
Ultralytics, MMDetection, YOLOP, or DeepStream-Yolo and does not need this
repository's TensorFlow/TFLite scripts.
- The task is only generic object-detection theory, annotation labeling policy,
or Android camera app development unrelated to this repo's TFLite assets.
- The user wants Creator-mode skill production or refresh work; route that to
the appropriate Creator workflow instead of using this operating skill.