alessiosavi

Hi Clash, Now i'm trying to understand which type of neural network suits better for recognize the 68 points extract from the face. So the work that you find here...

I've made some tests. During the predict phase, the most time consuming process is the face encoding.  As you can see, encode two face cost ~3s on my hardware...

Hi Sir, Thank you for the interest in the project! _You was completely right!_ The problems related to the `jitter` parameter, was caused from the KNN that was not able...

Hi @ClashLuke, The `num_jitter` is related to the number of times to re-sample the face when calculating encoding. If num_jitters>1 then each face will be randomly jittered slightly num_jitters times,...

Hi @ClashLuke, thank you for the interest and sorry for the late response. I'm very busy these days and i can only contribute in the weekend. Of course, you can...

Hi Clash! Thank you for the effort of the analysis! I'm here for explanation if you need some tips on the code. Of course, we can tune the hyperparameters in...

Hi Clash, I'm going to rewrite the "backen engine" from scratch using `dlib` and `tensorflow`. I'm going to update the repo in the next month. I'm testing the neural network...

I've no issue in Colab with `GPU` runtime and the following packages: ```bash !pip install git+https://github.com/keras-team/keras-nlp.git keras-core tensorflow[and-cuda] --upgrade ``` ```python import keras_core as keras import tensorflow as tf import...