FaceNet is a neural network for face re-identification based on the Inception architecture.
It regresses a face feature vector (embedding) that can be matched against a database of known face vectors. In this version the embedding has dimension 512. The weights come from the DeepFace implementation.
Note: This is not a face detection model. Faces must be detected and cropped before being passed to FaceNet.
Key Features
Compact 512-dimensional face embedding suitable for verification and identification tasks.
Fully quantized int8 model with uint8 input and float32 output.
Compiled for i.MX 93 Ethos-U65 NPU and converted for i.MX 95 / i.MX 952 Neutron NPU.
Model Description
Modifications
The original Keras model from the DeepFace library is converted directly to TensorFlow Lite with full int8 quantization using 100 calibration images. The input type is uint8 (values 0-255) and the output type is float32.
Model Information
Information
Value
Input shape
RGB face crop (1, 160, 160, 3), uint8
Output shape
Face embedding vector (1, 512), float32
FLOPS
2.84 GOPS
Number of parameters
23,497,424
Source framework
TensorFlow/Keras (DeepFace 0.0.75)
Target platform
MPUs (i.MX 8M Plus, i.MX 93, i.MX 95, i.MX 952)
Tested Configurations
The quantized int8 model has been tested on i.MX 8M, i.MX 93, i.MX 95 and i.MX 952 using benchmark-model
(see i.MX Machine Learning User Guide).
Training and Evaluation
The original model was trained and evaluated on the LFW dataset, achieving 99.65% accuracy according to the DeepFace source.
We re-evaluated the model on LFW deep-funneled images from the Kaggle LFW dataset using the pairsDevTest split (1000 pairs, cosine distance threshold 0.6):
Model
Accuracy
FaceNet512 Keras float32
97.5%
FaceNet512 TensorFlow Lite int8
97.5%
The evaluation script is evaluate.py.
Conversion/Quantization
The original Keras model is converted directly to TensorFlow Lite with full int8 post-training quantization. 100 random images are used as calibration data. The conversion is performed using TensorFlow 2.x with:
inference_input_type = tf.uint8
inference_output_type = tf.float32
OpsSet.TFLITE_BUILTINS_INT8
The i.MX 93 model is compiled from the int8 TFLite model using Vela tag lf-6.18.20_2.0.0 with --accelerator-config ethos-u65-512 --optimise Performance.
The i.MX 95 and i.MX 952 models are converted using eIQ Neutron SDK version 3.1.3.
Download and Run
To regenerate the TFLite model fully quantized in int8 (uint8 input, float32 output), run:
bash recipe.sh
An example showing how to compute the cosine similarity between two face images is in example.py:
