Python scripts to detect faces in Python with the BlazeFace Tensorflow Lite models

Last update: Nov 17, 2022

Overview

BlazeFace TFLite Inference

Python scripts to detect faces using Python with the BlazeFace Tensorflow Lite models. Tested on Windows 10, Tensorflow 2.4.0 (Python 3.8).

Requirements

OpenCV, Numpy and tensorflow 2. pafy and youtube-dl are required for youtube video inference.

Installation

conda create -n blazeFace python=3.8 -y
conda activate blazeFace
conda install numpy opencv tensorflow -y
pip install pafy youtube-dl

Model types

Front: Input size 128 x 128, faster but lower accuracy.
Back: Input size 256 x 256, higher accuracy but slower.

Examples

Image inference:

python imageFaceDetection.py

Webcam inference:

python webcamFaceDetection.py

Video inference:

python videoFaceDetection.py

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Comments

What is the flops, quantization precision and standard inference time for the provided tflight models?

Hi,

I want to know what is flops, quantization precision, and standard inference time for the provided models: face_detection_back.tflite and face_detection_front.tflite?

can you please provide the above infos?. Thanks!

opened by Jargon4072 1

Owner

Ibai Gorordo

GitHub

A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]

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