Making computers identify and understand what they are looking at in digital images is an ongoing challenge. Recent years have seen notable increases in the accuracy and speed of object detection due to deep learning and new applications of neural networks. In order to make it easier for developers to take advantage of these techniques Tryo Labs built Luminoth. In this interview Joaquín Alori explains how how Luminoth works, how it can be used in your projects, and how it compares to API oriented services for computer vision.
Do you want to try out some of the tools and applications that you heard about on Podcast.__init__? Do you have a side project that you want to share with the world? Check out Linode at linode.com/podcastinit or use the code podcastinit2019 and get a $20 credit to try out their fast and reliable Linux virtual servers. They’ve got lightning fast networking and SSD servers with plenty of power and storage to run whatever you want to experiment on.
With GoCD’s comprehensive pipeline modeling, you can model complex workflows for multiple teams with ease. And GoCD’s Value Stream Map lets you track a change from commit to deploy at a glance.
GoCD’s real power is in the visibility it provides over your end-to-end workflow. So you get complete control of and visibility into your deployments, across multiple teams.
Say goodbye to deployment panic and hello to consistent, predictable deliveries.
To learn more about GoCD, visit gocd.org for a free download. Professional Support and enterprise add-ons, including disaster recovery, are available.
Datadog is a powerful, easy to use service for gaining comprehensive visibility into the state of your applications. The easy to install Python agent lets you collect system metrics and log data, supports integrations with all of your services, and distributed tracing. Their customizable dashboards and interactive graphs make finding and fixing performance issues fast and easy, and their machine-learning driven alerting ensures that you always know what is happening in your systems.
If you need even more detail about how your application is functioning you can track custom metrics, and their Application Performance Monitoring (APM) tools let you track the flow of requests through your stack. Go to podcastinit.com/datadog today to start your free 14 day trial and get an awesome new T-shirt.
- Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
- When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
- For complete visibility into your application stack, deployment tracking, and powerful alerting, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix bugs in no time. Go to podcastinit.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.
- To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
- Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
- Your host as usual is Tobias Macey and today I’m interviewing Joaquín Alori about Luminoth, a deep learning toolkit for computer vision in Python
- How did you get introduced to Python?
- What is Luminoth and what was your motivation for creating it?
- Computer vision has been a focus of AI research for decades. How do current approaches with deep learning compare to previous generations of tooling?
- What are some of the most difficult problems in visual processing that still need to be solved?
- What are the limitations of Luminoth for building a computer vision application and how do they differ from the capabilities of something built with a prior generation of tooling such as OpenCV?
- For someone who is interested in using Luminoth in their project what is the current workflow?
- How do the capabilities of Luminoth compare with some of the various service based options such as Rekognition for Amazon or the Cloud Vision API from Google?
- What are some of the motivations for using Luminoth in place of these services?
- What are some of the highest priority features that you are focusing on implementing in Luminoth?
- When is Luminoth the wrong choice for a computer vision application and what are some of the strongest alternatives at the moment?
Keep In Touch
- Luminoth Release Announcement
- Tryo Labs
- Industrial Engineering
- Manufacturing Engineering
- Elon Musk
- Artificial Intelligence
- Deep Learning
- Neural Networks
- Object Detection
- Image Segmentation
- Convolutional Neural Network
- Recurrent Neural Network
- Back Propagation
- Geoff Hinton
- Capsule Networks
- Generative Adversarial Networks
- SVM (Support Vector Machine)
- Haar Classifiers
- GPU (Graphics Processing Unit)
- Cloud Vision API
- TensorFlow Object Detection API