Info

This branch provides EdgeTPU support complement to branch tf-only-export. 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 instead of git pull.

Usage

1. Git clone yolov5 and checkout tf-edgetpu

git clone https://github.com/zldrobit/yolov5.git
cd yolov5
git checkout tf-edgetpu

and download pretrained weights from

https://github.com/ultralytics/yolov5.git

2. Install requirements

pip install -r requirements.txt
pip install tensorflow==2.4.1

3. Convert and verify

• Convert weights to int8 TFLite model, and verify it with (Post-Training Quantization needs train or val images from COCO 2017 dataset)

python3 models/tf.py --weights weights/yolov5s.pt --cfg models/yolov5s.yaml --img 320 --no-tfl-detect --tfl-int8 --tf-raw-resize --source /data/dataset/coco/coco2017/train2017 --ncalib 100
python3 detect.py --weight weights/yolov5s-int8.tflite --img 320 --tfl-int8 --tfl-detect

• Convert full int8 TFLite model to Edge TPU and verify it with

# need Edge TPU runtime https://coral.ai/software/#edgetpu-runtime
# and Edge TPU compiler https://coral.ai/software/#debian-packages
edgetpu_compiler -s -a -o weights weights/yolov5s-int8.tflite
python3 detect.py --weights weights/yolov5s-int8_edgetpu.tflite --edgetpu --tfl-int8 --tfl-detect --img 320

If you have further question, plz ask in ultralytics#3630