Yolov5 TFLite — Inferencing in Mobile Devices

Learning Outcome:


git clone https://github.com/Techyhans/yolov5-portholes.git
cd yolov5
pip install -r requirements.txt

Train custom Yolov5 model

path: data/sample
train: train/images
val: valid/images
test: test/images
nc: 2
names: ['CoW', 'chanterelle']
python train.py --img 640 --batch 16 --epochs 300 --data sample/data.yaml --weights yolov5s.pt

Convert Pytorch (.pt) model file to TFLite (.tflite)

python export.py --weights best.pt --include tflite --tf-nms --agnostic-nms

Metadata Writer

"name": "ObjectDetector",
"description": "Identify which of a known set of objects might be present and provide information about their positions within the given image or a video stream."
python metadata_writer_v1.py --model_file best-fp16.tflite --label_file labels.txt
# Your model details here
model_path = ‘best-fp16.tflite’
label_path = ‘labels.txt’
model_meta.name = “Model name”
model_meta.description = (
“decription line …”
“decription line …”
python metadata_writer_v2.py




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Draden Liang Han Sheng

Full Stack AI Application Development | Computer Vision | Deep Learning | Edge Devices