Python Deep Learning Projects

Evaluation

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 ...