Region of interest pooling in TensorFlow – example

Region of interest pooling in TensorFlow – example

In the previous post we explained what region of interest pooling (RoI pooling for short) is. In this one, we present an example of applying RoI pooling in TensorFlow. We base it on our custom RoI pooling TensorFlow operation. We also use Neptune as a support in our experiment performance tracking.

Neptune 1.5 – Python 3 support, simplified CLI, compact view

At the end of April 2017, deepsense.ai released a new version of Neptune, the DevOps platform for data scientists. Neptune 1.5 introduces a range of new features and improvements, including support for Python 3, simplification of Neptune CLI, offline execution, …

Deep learning for satellite imagery via image segmentation

Deep learning for satellite imagery via image segmentation

We describe 4th place solution based on image segmentation and deep learning for Dstl Satellite Imagery Feature Detection competition.

Training XGBoost with R and Neptune

We present the training of an XGBoost model and evaluation of the results in an example analysis of bank customer data, using R with Neptune.

Neptune machine learning platform: grid search, R & Java support

In February we released a new version of Neptune, our machine learning platform for data scientists, supporting them in more efficient experiment management and monitoring. The latest 1.4 release introduces new features, like grid search — a hyperparameter optimization method and support for R and Java programming languages. Grid Search The first major feature introduced […]

Region of interest pooling explained

Region of interest pooling (also known as RoI pooling) is an operation widely used in object detection tasks using convolutional neural networks. For example, to detect multiple cars and pedestrians in a single image. Its purpose is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7×7). We’ve just […]

Machine Learning for Applications in Manufacturing

Modern manufacturing technology is starting to incorporate machine learning throughout the production process. Predictive algorithms are being used to plan machine maintenance adaptively rather than on a fixed schedule. Meanwhile, quality control is becoming more and more automated, with adaptive algorithms that learn to recognize correctly manufactured products and reject defects. In this post we […]

An internal validation leaderboard in Neptune

An internal validation leaderboard in Neptune

Internal validation is a useful tool for comparing results of experiments performed by team members in any business or research task. It can also be a valuable complement of public leaderboards attached to machine learning competitions on platforms like Kaggle. In this post, we present how to build an internal validation leaderboard using Python scripts […]

Neptune 1.3 with TensorFlow integration and experiments in Docker

We’re happy to announce that a new version of Neptune became available this month. The latest 1.3 release of deepsense.ai’s machine learning platform introduces powerful new features and improvements. This release’s key added features are: integration with TensorFlow and running Neptune experiments in Docker containers.

Machine Learning Models Predicting Dangerous Seismic Events

Machine Learning Models Predicting Dangerous Seismic Events

Underground mining poses a number of threats including fires, methane outbreaks or seismic tremors and bumps. An automatic system for predicting and alerting against such dangerous events is of utmost importance – and also a great challenge for data scientists and their machine learning models. This was the inspiration for the organizers of AAIA’16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines.