This branch provides detection and Android code complement to branch
Since the release of YOLOv5 v6.0, TFLite models can be exported by tf-only-export
.export.py
in ultralytics' master branch. Using models/tf.py
to export models is deprecated, and this repo is mainly for Anrdroid demo app.
models/tf.py
uses TF2 API to construct a tf.Keras model according to *.yaml
config files and reads weights from *.pt
, without using ONNX.
Because this branch persistently rebases to master branch of ultralytics/yolov5, use git pull --rebase
or git pull -f
instead of git pull
.
git clone https://github.com/ultralytics/yolov5.git
cd yolov5
- Convert weights to fp16 TFLite model, and verify it with
python export.py --weights yolov5s.pt --include tflite --img 320
python detect.py --weights yolov5s-fp16.tflite --img 320
or
- Convert weights to int8 TFLite model, and verify it with
python export.py --weights yolov5s.pt --include tflite --int8 --img 320 --data data/coco128.yaml
python detect.py --weights yolov5s-int8.tflite --img 320
Note that:
- int8 quantization needs dataset images to calibrate weights and activations, and the default COCO128 dataset is downloaded automatically.
- Change
--img
to the input resolution of your model, if it isn't 320.
git clone https://github.com/zldrobit/yolov5.git yolov5-android
inputSize
to--img
output_width
according to new/oldinputSize
ratioanchors
tom.anchor_grid
as ultralytics#1127 (comment) in android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/DetectorFactory.javalabelFilename
according to the classes of the model in https://github.com/zldrobit/yolov5/blob/522d65e848d3e5a378eb0f29a9fbb204221400e8/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/DetectorFactory.java#L19-L48.
Then run the program in Android Studio.
TODO:
- Add NNAPI support
EDIT:
- Update according YOLOv5 v6.0 release
If you have further question, plz ask in ultralytics#1127