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.
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, …
We describe 4th place solution based on image segmentation and deep learning for Dstl Satellite Imagery Feature Detection competition.
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.
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 in Neptune 1.4 is support for grid search, which is one of the most popular hyperparameter optimization method. You can read […]
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 released an open-source implementation of RoI pooling layer for TensorFlow (you can find it here). […]
A few days ago we released Seahorse 1.4, an enhanced version of our machine learning, Big Data manipulation and data visualization product. This release also comes with an SDK – a Scala toolkit for creating new custom operations to be used in Seahorse. As a showcase, we will create a custom Geospatial operation with GeoJson and make a simple Seahorse workflow using it. This use-case is inspired by Example […]
In the latest Seahorse release we introduced the scheduling of Spark jobs. We will show you how to use it to regularly collect data and send reports generated from that data via email. Use case Let’s say that we have a local meteo station and the data from this station is uploaded automatically to Google Sheets. That is, it contains only one row (i.e. row number 2) of data which always […]
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 and the Neptune environment. As an example of a use case, we will take the well known classification dataset […]
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.