Hyperparameters optimization is an integral part of working on data science projects. But the more parameters we have to optimize, the more difficult it is to do it manually. To speed up project development, we may want to automate this work.

In this article, we will explore Optuna. It is an open-source framework for efficient and automatic hyperparameter optimization. We will take a closer look at its components and optimization methods. Additionally, we will learn how to integrate Optuna into PyTorch ( Google Colab).

Most machine learning algorithms have configurable hyperparameters. They control the algorithm’s behavior and directly affect the…


Last year, we published an article in our blog explaining how facial recognition works with face masks. There we presented our facial recognition model results, which achieves 58% accuracy on masked face images.

Since then, we have modified our algorithm for generating synthetic images of faces in masks. Our model’s results on the accuracy metric have significantly improved. We also prepared a dataset with authentic pictures of people wearing masks. It can be used as a benchmark for face recognition systems in a coronavirus pandemic era.

Why It Matters

Most people wear face masks during the COVID-19 pandemic. Especially in public places, where…

From NLP to CV: Vision Transformer

For a long time, convolutional neural networks (CNNs) have been the de facto standard in computer vision. On the other hand, in natural language processing (NLP), Transformer is today’s prevalent architecture. Its spectacular success in the language domain inspired scientists to look for ways to adapt them for computer vision.

Vision Transformer ( ViT) is proposed in the paper: An image is worth 16x16 words: transformers for image recognition at scale. It is the convolution-free architecture where transformers are applied to the image classification task. The idea is to represent an image as a sequence of image patches (tokens). …


Probably every data scientist has come across a situation where you have created many different models, with different parameters or the entire architecture, and started experimenting.

Aside from the model architecture, you wanted to experiment with the choice of optimizer, learning rate, number of epochs, and so on. Thus, in fact, you will have many different experiments and it will become more and more difficult to structure the results obtained. In this article, we’ll show you how to properly and conveniently manage and log your machine and deep learning experiments.

Today there are many utilities that allow you to conveniently…

In this article, we reviewed three different pipelines for human pose estimation detectors, namely the most popular detectors on Github (OpenPose and Simple Pose) and a lighter weight model from TensorFlow — PoseNet (tflite).

We tested each pipeline on three different images of a person. The detectors’ following parameters were assessed: the ability to find key points in the image, confidence in predictions, the total size of the models on disk, and the predictive time on the CPU / GPU (Google Collaboratory). All our experiments are located on GitHub.

Original images:


The key feature of this pipeline is the use…

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