Announcing Luminoth 0.1: new object detection models, checkpoints and more!
On previous blog posts, we’ve talked about Luminoth, our own open-source computer vision toolkit, built upon Tensorflow and Sonnet. Well, we just released a new version, so this is a good time as any to dive into it! Version 0.1 brings several very exciting improvements: An implementation of the Single Shot Multibox Detector (SSD) model was added, a much faster (although less accurate) object detector than the already-included Faster R-CNN.
Hosting an Object Detection workshop and sponsoring at the PyImageConf
A couple of weeks ago we received an invitation from Adrian Rosebrock to give a workshop at the first edition of the PyImageConf in San Francisco. Our first reaction was of excitement. We were already aware of the great speakers that were featured (François Chollet, Davis King, Adam Geitgey, Adrian Rosebrock himself, among others), and we really liked the format of the event: practical & intimate. Moreover, the conference is centered on three fields we’re pretty familiar with: Deep Learning, Computer Vision & Python.
Introduction to Visual Question Answering: Datasets, Approaches and Evaluation
Historically, building a system that can answer natural language questions about any image has been considered a very ambitious goal. Imagine a system that, given the image below, could answer these questions: What is in the image? Are there any humans? What sport is being played? Who has the ball? How many players are in the image? Who are the teams? Is it raining? Argentina facing England in 1986Image source
Faster R-CNN: Down the rabbit hole of modern object detection
Previously, we talked about object detection, what it is and how it has been recently tackled using deep learning. If you haven’t read our previous blog post, we suggest you take a look at it before continuing. Last year, we decided to get into Faster R-CNN, reading the original paper, and all the referenced papers (and so on and on) until we got a clear understanding of how it works and how to implement it.
Deep Learning for Natural Language Processing (NLP): Advancements & Trends
Over the past few years, Deep Learning (DL) architectures and algorithms have made impressive advances in fields such as image recognition and speech processing. Their application to Natural Language Processing (NLP) was less impressive at first, but has now proven to make significant contributions, yielding state-of-the-art results for some common NLP tasks. Named entity recognition (NER), part of speech (POS) tagging or sentiment analysis are some of the problems where neural network models have outperformed traditional approaches.
Our ODSC talks in video
As we previously announced, the last couple of weeks we’ve been launching Luminoth, our brand new open source toolkit for Computer Vision. As part of this launch, we gave talks in the Open Data Science Conference (ODSC), both at the London and San Francisco editions. The events were a great success, as in 5 days they accumulated around 5000 data scientists, while hosting more than 200 talks from renowned people in the academia and industry.
Launching Luminoth: our open source computer vision toolkit
After a few months working in stealth mode, we are very proud to launch our Deep Learning initiative: luminoth.ai Luminoth is an open source toolkit for computer vision. Currently, we support object detection and image classification, but we are aiming for much more. It is built in Python, using Google’s Machine Intelligence framework, TensorFlow; and Sonnet, a very useful library built by DeepMind for building complex neural networks with reusable components.
We'll be speaking at ODSC San Francisco & London, presenting our Deep Learning R&D findings
As we have become accustomed to, exciting things are happening in the Machine Learning ecosystem. One can easily argue that it is novel applications of Deep Learning which are leading this excitement. Real world uses of Deep Learning are growing day to day: improving machine text translation, music generation, style transfer, object detection and cooler than ever generative models are just a few examples. The vast number of new applications, and the pace of improvement over existing ones, make it harder than ever to keep up to date with the latest advancements in the field.
Object detection: an overview in the age of Deep Learning
There’s no shortage of interesting problems in computer vision, from simple image classification to 3D-pose estimation. One of the problems we’re most interested in and have worked on a bunch is object detection. Like many other computer vision problems, there still isn’t an obvious or even “best” way to approach the problem, meaning there’s still much room for improvement. Before getting into object detection, let’s do a quick rundown of the most common problems in the field.
Finding the right representation for your NLP data
When considering what information is important for a certain decision procedure (say, a classification task), there’s an interesting gap between what’s theoretically —that is, actually— important on the one hand and what gives good results in practice as input to machine learning (ML) algorithms, on the other. Let’s look at sentiment analysis tools as an example. Expression of sentiment is a pragmatic phenomenon. To predict it correctly, we need to know both the meaning of the sentences and the context in which those sentences appeared.