We will evaluate the performance of the trained model. To do that, we will execute the following command:
docker run -v $PWD:/facerecognition \
-e PYTHONPATH=$PYTHONPATH:/facerecognition \
-it hellorahulk/facerecognition \
python3 /facerecognition/facenet/train_classifier.py \
--input-dir /facerecognition/output/intermediate \
--model-path /facerecognition/pre-model/Resnet-185253.pb \
--classifier-path /facerecognition/output/classifier.pkl \
--num-threads 16 \
--num-epochs 2 \
--min-num-images-per-class 10 \
Once the execution is completed, you will see predictions with a confidence score, as shown in the following screenshot:
We can see that the model is able to predict with 99.5% accuracy. It is also relatively fast.
